Initial commit

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.gitignore

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+# https://git-scm.com/docs/gitignore
+
+# gradio
+flagged/
+gradio_cached_examples/
+
+# mypy
+.mypy_cache/
+
+# python
+__pycache__/
+
+# venv
+.venv/

.python-version

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+3.11

LICENSE.txt

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+https://opensource.org/license/mit/

README.md

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+---
+title: Resume
+emoji: 🧑🏻‍💻
+colorFrom: grey
+colorTo: green
+sdk: gradio
+python_version: 3.11
+sdk_version: 3.46.0
+app_file: app.py
+fullWidth: false
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+"""Answer questions about my resume."""
+
+# %% IMPORTS
+
+import logging
+
+import gradio as gr
+import tiktoken
+
+import lib
+
+# %% LOGGING
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="[%(asctime)s][%(levelname)s] %(message)s",
+)
+
+# %% CONFIGS
+
+TITLE = "Fmind AI Assistant"
+THEME = "glass"
+CLIENT = lib.get_database_client(path="database")
+ENCODING = tiktoken.get_encoding("cl100k_base")
+FUNCTION = lib.get_embedding_function()
+COLLECTION = CLIENT.get_collection(
+    name=lib.DATABASE_COLLECTION,
+    embedding_function=FUNCTION,
+)
+EXAMPLES = [
+    "Who is Médéric Hurier (Fmind)?",
+    "Is Fmind open to new opportunities?",
+    "What is Médéric's most recent degree?",
+    "What is Médéric's latest work experience?",
+    "Is Médéric proficient in Python programming?",
+]
+
+# %% FUNCTIONS
+
+
+def answer(message: str, history: list[str]) -> str:
+    """Answer questions about my resume."""
+    tokens = ENCODING.encode(message)
+    print("History:", len(history))
+    print("Tokens:", len(tokens))
+    return message
+
+
+# %% INTERFACES
+
+interface = gr.ChatInterface(
+    fn=answer,
+    theme=THEME,
+    title=TITLE,
+    examples=EXAMPLES,
+    clear_btn=None,
+    retry_btn=None,
+    undo_btn=None,
+)
+
+# %% ENTRYPOINTS
+
+if __name__ == "__main__":
+    interface.launch()

database.py

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+#!/usr/bin/env python3
+"""Manage the project database."""
+
+# %% IMPORTS
+
+import argparse
+import logging
+import re
+import sys
+import typing as T
+
+import lib
+
+# %% LOGGING
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="[%(asctime)s][%(levelname)s] %(message)s",
+)
+
+# %% PARSING
+
+PARSER = argparse.ArgumentParser(description=__doc__)
+PARSER.add_argument("--database", type=str, required=True)
+PARSER.add_argument("files", type=argparse.FileType("r"), nargs="+")
+
+# %% FUNCTIONS
+
+
+def segment_text(text: str, pattern: str) -> T.Iterator[tuple[str, str]]:
+    """Segment the text in title and content pair by pattern."""
+    splits = re.split(pattern, text, flags=re.MULTILINE)
+    pairs = zip(splits[1::2], splits[2::2])
+    return pairs
+
+
+def import_file(file: T.TextIO, collection: lib.Collection) -> int:
+    """Import a markdown file to a database collection."""
+    imported = 0
+    text = file.read()
+    filename = file.name
+    segments_h1 = segment_text(text=text, pattern=r"^# (.+)")
+    for h1, h1_text in segments_h1:
+        logging.debug('\t- H1: "%s" (%d)', h1, len(h1_text))
+        segments_h2 = segment_text(text=h1_text, pattern=r"^## (.+)")
+        for h2, content in segments_h2:
+            logging.debug('\t\t- H2: "%s" (%d)', h2, len(content))
+            id_ = f"{filename} # {h1} ## {h2}"  # unique doc id
+            document = f"# {h1}\n\n## {h2}\n\n{content.strip()}"
+            metadata = {"filename": filename, "h1": h1, "h2": h2}
+            assert len(content) < 8000, f"Content is too long: #{h1} ##{h2}"
+            collection.add(ids=id_, documents=document, metadatas=metadata)
+            imported += len(document)
+    return imported
+
+
+def main(args: list[str] | None = None) -> int:
+    """Main function of the script."""
+    # parsing
+    opts = PARSER.parse_args(args)
+    # database
+    database_path = opts.database
+    logging.info("Database path: %s", database_path)
+    client = lib.get_database_client(path=database_path)
+    logging.info("- Reseting database client: %s", client.reset())
+    # embedding
+    embedding_function = lib.get_embedding_function()
+    logging.info("Embedding function: %s", embedding_function)
+    # collection
+    database_collection = lib.DATABASE_COLLECTION
+    logging.info("Database collection: %s", database_collection)
+    collection = client.create_collection(
+        name=database_collection, embedding_function=embedding_function
+    )
+    # files
+    for i, file in enumerate(opts.files):
+        logging.info("Importing file %d: %s", i, file.name)
+        imported = import_file(file=file, collection=collection)
+        logging.info("- Docs imported from file %s: %d chars", i, imported)
+    # return
+    return 0
+
+
+# %% ENTRYPOINTS
+
+
+if __name__ == "__main__":
+    sys.exit(main())

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files/linkedin.md

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+# Profile
+
+## Overview
+
+- First name: Médéric
+- Last name: HURIER
+- Pseudo: Fmind
+- Followers: 4K
+- Location: Luxembourg, Luxembourg
+- Education: University of Luxembourg
+- Current position: Decathlon Technology
+- Public URL: www.linkedin.com/in/fmind-dev
+- Industry: Technology, Information and Internet
+- Address: 28 Avenue François Clément, 5612 Mondorf-les-Bains, Luxembourg
+- Headline: Freelancer | AI/ML/MLOps Engineer | Data Scientist | MLOps Community Organizer | OpenClassrooms Mentor | Hacker | PhD
+
+## Websites
+
+- Company website: https://www.fmind.dev
+- Twitter: https://twitter.com/fmind_dev
+- Portfolio: https://github.com/fmind/
+- Blog: https://fmind.medium.com/
+
+## About
+
+Note: I'm not available to work on new missions until the 1st of
+September 2024. Thank you for your understanding.
+
+When I worked as a teacher, I told my students that Artificial
+Intelligence and Machine Learning are the most effective levers to make
+a difference. Every day, new AI and ML solutions are released to empower
+companies and individuals alike. The question is: Is your business ready
+to provide the best AI/ML products for your customers?
+
+I'm a professional Machine Learning Engineer, Data Scientist, and MLOps
+ready to assist you in this quest. I've completed a Ph.D. in Machine
+Learning and several high-end AI/ML certifications to help you build
+leading data-driven services. My past experiences include working with
+companies like Google, BNP Paribas, ArcelorMittal, the European
+Commission, and Decathlon to frame their needs, create state-of-the-art
+models and deliver AI/ML artifacts at scale.
+
+I now work as a freelancer in Luxembourg, and I can carry out missions
+remotely in other European countries. You can get in touch with me on
+LinkedIn or at [email protected]. I'll be happy to collaborate with you
+or discuss your favored AI/ML topics in the MLOps Community.
+
+# Experience
+
+## Lead MLOps Engineer
+
+- Company: Decathlon Technology
+- Period: Sep 2022 - Present
+- Location: Luxembourg (Hybrid)
+- Mission: Continue the design and implementation of Decathlon's MLOps platform with Databricks and AWS.
+- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · ChatGPT · Agile Methodology · MLflow · Docker · Jira · Git · DataBricks · Apache Airflow · AWS SageMaker · Terraform · Apache Spark · Kubernetes
+
+## Mentor for aspiring Data Scientist and AI/ML Engineer
+
+- Company: OpenClassrooms
+- Period: Mar 2023 - Present
+- Location: France (Remote)
+- Mission: Tutoring adult students to become data scientists specializing in machine learning.
+- Courses:
+    - https://openclassrooms.com/fr/paths/793-data-scientist
+    - https://openclassrooms.com/fr/paths/794-machine-learning-engineer
+    - https://openclassrooms.com/fr/paths/795-ai-engineer
+- Skills: Artificial Intelligence (AI) · Machine Learning · Python · Deep Learning · Data Science · Statistics · ChatGPT · Big Data · Jupyter · Pandas · Git · Natural Language Processing (NLP) · Scikit-Learn
+
+## Senior Data Scientist & Project Manager
+
+- Company: Cronos Europa
+- Customer: European Commission
+- Period: Dec 2021 - Sep 2022
+- Location: Luxembourg (Hybrid)
+- Mission: Enhance the ARACHNE risk scoring tool (fraud detection).
+- Main tasks and responsibilities:
+    - Develop a new version of Arachne using data mining techniques
+    - Manage the development of the Arachne PoC/Project (SCRUM)
+    - Assist data scientists in their projects (Virtual Assistant, NLP,
+    …)
+- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · Deep Learning · Data Science · Big Data · Agile Methodology · Project Management · Functional Programming · Jupyter · Pandas · Docker · Jira · Git · PostgreSQL · AWS SageMaker · Flask · UML · API REST · Terraform · Transformers · Natural Language Processing (NLP) · Data Engineering · Microsoft Azure Machine Learning · Neo4j
+
+## Project Manager & Machine Learning Engineer
+
+- Company: SFEIR Luxembourg
+- Period: Dec 2020 - Dec 2021
+- Location: Luxembourg (Remote)
+- Mission: Design and implement the next ML/MLOps platform on AWS and GCP.
+- Main tasks and responsibilities:
+    - Design the functional & technical architecture of the platform
+    - Manage the MLOps@Decathlon initiative (tasks, plannings)
+    - Select the vendor solutions based on a user need analysis
+    - Communicate the progress and success to stack-holders
+    - Assist data scientists in their project (audience, forecast)
+- Technical stack:
+    - Data Science: Python, TensorFlow, Spark, sklearn, Jupyter, Airflow
+    - Management: Google Workspace, Jira, UML, Terraform, Jenkins
+    - Environments: AWS (SageMaker), GCP (Vertex AI), DataBricks
+- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · Deep Learning · Data Science · Big Data · Agile Methodology · Project Management · Functional Programming · Google Cloud Platform (GCP) · Tensorflow · MLflow · Jupyter · Pandas · Docker · Keras · Jira · Git · DataBricks · Apache Airflow · AWS SageMaker · Flask · UML · Terraform · Data Engineering · Vertex AI (GCP) · Apache Spark · Scikit-Learn · Kubernetes
+
+## Data Scientist
+
+- Company: SFEIR
+- Period: Oct 2020 - Nov 2020
+- Location: Luxembourg (Remote)
+- Mission: Improve the visibility and assets of SFEIR's Data Team.
+- Main tasks and responsibilities:
+    - Design and create technical interviews for recruiting data scientists.
+    - Become a Professional Machine Learning Engineer on Google Cloud.
+    - Propose a strategy to improve the online visibility of SFEIR data team.
+    - Share knowledge about data trends with non-technical staff members.
+    - Create a group to write tutorials and kata on AI/ML for SFEIR developers.
+- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · Deep Learning · Data Science · Agile Methodology · Functional Programming · Google Cloud Platform (GCP) · Tensorflow · Jupyter · Pandas · Keras · Git · MongoDB · Vertex AI (GCP) · Apache Spark · Scikit-Learn
+
+## Data Scientist
+
+- Company: SFEIR
+- Customer: ArcelorMittal
+- Period: Jan 2020 - Sep 2020
+- Location: Luxembourg (Remote)
+- Mission: Train and optimize machine learning models to recommend steel prices.
+- Main tasks and responsibilities:
+    - Create and fine-tune machine-learning models (tree-based)
+    - Evaluate the performance of the model on real datasets
+    - Communicate the results to business stack-holders
+- Technical stack:
+    - Data Science: Python, XGBoost, sklearn, Jupyter, SQL
+    - Analytics: Matplotlib, Seaborn, Tableau, Plotly, Dash
+    - Environment: MS-SQL, Azure Cloud, Jira, Papermill
+- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · Data Science · Agile Methodology · Functional Programming · Jupyter · Pandas · Jira · Git · Natural Language Processing (NLP) · Scikit-Learn
+
+## Research And Development Specialist
+
+- Company: University of Luxembourg
+- Period: Sep 2019 - Jan 2020
+- Location: Luxembourg
+- Mission: Management and development of Natural Language Understanding (NLU) projects for BGL BNP Paribas.
+- Skills: Artificial Intelligence (AI) · Machine Learning · Python · Data Science · Big Data · Functional Programming · Tensorflow · Jupyter · Pandas · Docker · Git · PostgreSQL · Ansible · Flask · UML · JSON · API REST · Transformers · Natural Language Processing (NLP) · Apache Spark · Scikit-Learn
+
+## Doctoral researcher
+
+- Company: University of Luxembourg
+- Period: Sep 2015 - Jan 2020
+- Location: Luxembourg
+- Missions:
+    - Research activities focused on Android security and artificial intelligence.
+    - Teaching big data, machine learning and Android programming to students.
+    - Collaboration with Google, San Francisco on finding malicious Android artifacts.
+- Skills: Artificial Intelligence (AI) · Machine Learning · Python · Deep Learning · Data Science · Statistics · Big Data · Cybersecurity · Functional Programming · Jupyter · Pandas · Docker · Git · NoSQL · MongoDB · PostgreSQL · ElasticSearch · Ansible · Flask · JSON · Android · API REST · Natural Language Processing (NLP) · Data Engineering · Apache Spark · Scikit-Learn
+
+## Mentor for aspiring Data Scientist
+
+- Company: OpenClassrooms
+- Period: Aug 2018 - Dec 2019
+- Location: France
+- Mission: Tutoring adult students to become data scientists specializing in machine learning.
+- Skills: Artificial Intelligence (AI) · Machine Learning · Python · Data Science · Jupyter · Pandas · Git · Flask · JSON · API REST · Scikit-Learn
+
+## Security engineer specialized in log management and analysis
+
+- Company: Clearstream
+- Period: Apr 2014 - Aug 2015
+- Location: Luxembourg
+- Mission: Selection and deployment of a SIEM solution, participating in security incident response.
+- Skills: Python · Big Data · ISO 27001 · Cybersecurity · Jupyter · Pandas · Git · ElasticSearch · Data Engineering
+
+## Web developer and administrator
+
+- Company: Freaxmind
+- Period: Aug 2011 - Aug 2013
+- Location: France
+- Mission: Various contracts ranging from web development to software maintenance and debugging.
+- Skills: Python · Object Oriented Programming (POO) · Git · Ansible · Flask
+
+## Web Developer
+
+- Company: Toul'embal
+- Period: Jun 2012 - Aug 2012
+- Location: Toul, France
+- Mission: Extension of a Prestashop e-commerce website and creation a portfolio website with WordPress.
+- Skills: Object Oriented Programming (POO)
+
+## Web Programmer
+
+- Company: Empreinte Studio
+- Period: Oct 2010 - Aug 2011
+- Location: Épernay, France
+- Mission: Creation of modern website in PHP and MySQL with professional writers and graphic designers.
+- Skills: Object Oriented Programming (POO) · Git
+
+## Software Developer
+
+- Company: GEOVARIANCES
+- Period: Sep 2009 - Sep 2010
+- Location: Avon, France
+- Mission: Development of a geostatistic application in C++ and Qt with experienced software engineers.
+- Skills: Object Oriented Programming (POO) · Git · UML
+
+## Web Developer
+
+- Company: CV Champagne Nicolas Feuillatte
+- Period: Apr 2009 - Aug 2009
+- Location: Épernay, France
+- Mission: Integration of customer and share management modules to J.D. Edwards with PHP and Oracle.
+- Skills: Object Oriented Programming (POO)
+
+# Education
+
+## Doctor of Philosophy (PhD) in computer security and artificial intelligence
+
+- School: University of Luxembourg
+- Location: Luxembourg
+- Grade: Very Good
+- Period: 2015 - 2019
+- Activities and Societies: Teach Big Data and Android to students.
+- Thesis title: Creating better ground truth to further understand Android malware
+
+## Master's degree in computer and information systems security
+
+- School: UFR Mathématiques, Informatique, Mécanique et Automatique
+- Location: Metz (France)
+- Period: 2013 - 2014
+
+## Bachelor and master years in computer science applied to business informatics
+
+- School: UFR Mathématiques et Informatique de l’Université de Lorraine
+- Location: Nancy (France)
+- Period: 2011 - 2013
+
+## Professional bachelor's degree in computer security and databases
+
+- School: IUT Sénart-Fontainebleau
+- Location: Fontainebleau (France)
+- Period: 2009 - 2010
+
+## Professional bachelor’s degree in web development and integration
+
+- School: IUT Nancy-Charlemagne
+- Location: Nancy (France)
+- Period: 2008 - 2009
+
+## Technical degree in network and software development
+
+- School: Lycée François 1er
+- Location: Vitry-le-François (France)
+- Period: 2006 - 2008
+
+## Baccalauréat général degree in science, specialized in biology
+
+- School: Lycée Marc Chagall
+- Location: Reims (France)
+- Period: 2003 - 2006
+
+# Volunteer Experiences
+
+## MLOps Community Organizer (Luxembourg)
+
+- Community: MLOps Community
+- Role: Organizer
+- Location: Luxembourg
+- Period: Nov 2022 - present
+- Field: Science and Technology
+- Mission: Organize regular meetups and events for the MLOps Community.
+- Partners: AWS and the University of Luxembourg.
+- Link: https://www.meetup.com/luxembourg-mlops-community/
+
+# Licenses & Certifications
+
+## Machine Learning Associate
+
+- Issuer: Databricks
+- Issued: Nov 2022
+- Credential ID: 61461287
+
+## Databricks Lakehouse Fundamentals
+
+- Issuer: Databricks
+- Issued: Oct 2022
+- Credential ID: 61029028
+
+## Architecting with Google Kubernetes Engine Specialization
+
+- Issuer: Google
+- Issued: Sep 2022
+- Credential ID: WLU4DBPSQ4B5
+
+## Architecting with Google Kubernetes Engine: Foundations
+
+- Issuer: Google
+- Issued: Sep 2022
+- Credential ID: DFWAC6BXLNGL
+
+## Architecting with Google Kubernetes Engine: Production
+
+- Issuer: Google
+- Issued: Sep 2022
+- Credential ID: K5SZHUST5HP2
+
+## Architecting with Google Kubernetes Engine: Workloads
+
+- Issuer: Google
+- Issued: Sep 2022
+- Credential ID: ULJQAXGDVKYK
+
+## Google Cloud Fundamentals: Core Infrastructure
+
+- Issuer: Google
+- Issued: Sep 2022
+- Credential ID: 4CE8WQ6AWKFF
+
+## Iterative Tools for Data Scientists and Analysts
+
+- Issuer: Iterative
+- Issued: Aug 2022
+- Credential ID: 62fcb79418f51945ea
+
+## Azure Data Scientist Associate
+
+- Issuer: Microsoft
+- Issued: Jul 2022
+- Credential ID: 992564946
+
+## Azure Machine Learning for Data Scientists
+
+- Issuer: Microsoft
+- Issued: Jun 2022
+- Credential ID: MZKV7LSTQ9HX
+
+## Build and Operate Machine Learning Solutions with Azure Microsoft
+
+- Issuer: Microsoft
+- Issued: Jun 2022
+- Credential ID: 7FBX68MH272C
+
+## Create Machine Learning Models in Microsoft Azure
+
+- Issuer: Microsoft
+- Issued: Jun 2022
+- Credential ID: SHALM9PM3MPX
+
+## Microsoft Azure Data Scientist Associate - DP-100 Test Prep Specialization
+
+- Issuer: Microsoft
+- Issued: Jun 2022
+- Credential ID: L5P3TYLAYLLT
+
+## Perform data science with Azure Databricks
+
+- Issuer: Microsoft
+- Issued: Jun 2022
+- Credential ID: RQ7PLFYZVLXX
+
+## Prepare for DP-100: Data Science on Microsoft Azure Exam
+
+- Issuer: Microsoft
+- Issued: Jun 2022
+- Credential ID: K5KW27AVMYS2
+
+## Neo4j Graph Data Science Certified
+
+- Issuer: Neo4j
+- Issued: Apr 2022
+- Credential ID: 17351346
+
+## Microsoft Certified: Azure AI Fundamentals
+
+- Issuer: Microsoft
+- Issued: Jan 2022
+- Credential ID: 1098-0884
+
+## Artificial Intelligence on Microsoft Azure
+
+- Issuer: Microsoft
+- Issued: Dec 2021
+- Credential ID: Z8FSWXBSAGLD
+
+## Computer Vision in Microsoft Azure
+
+- Issuer: Microsoft
+- Issued: Dec 2021
+- Credential ID: KDDPYLKM2DA5
+
+## Microsoft Azure AI Fundamentals AI-900 Exam Prep Specialization
+
+- Issuer: Microsoft
+- Issued: Dec 2021
+- Credential ID: 96944QKZH9BU
+
+## Microsoft Azure Machine Learning
+
+- Issuer: Microsoft
+- Issued: Dec 2021
+- Credential ID: 32ES25845Q55
+
+## Natural Language Processing in Microsoft Azure
+
+- Issuer: Microsoft
+- Issued: Dec 2021
+- Credential ID: XVN23N8CKRGY
+
+## Preparing for AI-900: Microsoft Azure AI Fundamentals exam
+
+- Issuer: Microsoft
+- Issued: Dec 2021
+- Credential ID: YC83C22L8TBL
+
+## Build a Website on Google Cloud
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Build and Secure Networks in Google Cloud
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Create ML Models with BigQuery ML
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Create and Manage Cloud Resources
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Deploy to Kubernetes in Google Cloud
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Implement DevOps in Google Cloud
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Insights from Data with BigQuery
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Integrate with Machine Learning APIs
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Perform Foundational Infrastructure Tasks in Google Cloud
+
+- Issuer: Google
+- Issued: Aug 2021
+
+## Apache Spark Associate Developer
+
+- Issuer: Databricks
+- Issued: Jun 2021
+- Credential ID: fff03919-bbc9-304e-99ad-6f2ed47455ed
+
+## Scalable Machine Learning with Apache Spark
+
+- Issuer: Databricks
+- Issued: May 2021
+- Credential ID: 0f4adf96-0412-32f2-8232-fa50c51c9b47
+
+## Apache Spark Programming with Databricks
+
+- Issuer: Databricks
+- Issued: May 2021
+- Credential ID: 518a1d63-8894-3ab5-aaa5-50a9f169436c
+
+## Data Science Professional
+
+- Issuer: Databricks
+- Issued: May 2021
+- Credential ID: f05164e1-5a78-37f8-9c69-3e996fdbb21f
+
+## Delta Lake Fundamentals Accreditation
+
+- Issuer: Databricks
+- Issued: May 2021
+- Credential ID: 0d042e3f-50d3-3821-b064-f3c12ca6c17f
+
+## Deploying a Machine Learning Project with MLflow Projects
+
+- Issuer: Databricks
+- Issued: May 2021
+- Credential ID: 2afa0c7f-48f4-35af-b366-f7c77d2cd20a
+
+## Tracking Experiments with MLflow
+
+- Issuer: Databricks
+- Issued: May 2021
+- Credential ID: 0cbf87b7-e096-3792-a3b7-62d86aa6380d
+
+## Unified Data Analytics Accreditation
+
+- Issuer: Databricks
+- Issued: May 2021
+- Credential ID: afba5402-b5e4-3f9e-95f2-51d6bbb5fa64
+
+## ML Pipelines on Google Cloud
+
+- Issuer: Google
+- Issued: Mar 2021
+- Credential ID: FN5PYWX5PRCP
+
+## Introduction to Trading, Machine Learning & GCP
+
+- Issuer: Google
+- Issued: Nov 2020
+- Credential ID: YV9H5PF4YPLZ
+
+## MLOps (Machine Learning Operations) Fundamentals
+
+- Issuer: Google
+- Issued: Nov 2020
+- Credential ID: 4BDA24UL7K9Z
+
+## Machine Learning for Trading Specialization
+
+- Issuer: Google
+- Issued: Nov 2020
+- Credential ID: YSNPABSMV6JL
+
+## Reinforcement Learning for Trading Strategies
+
+- Issuer: Google
+- Issued: Nov 2020
+- Credential ID: VHKJLFPLLDLU
+
+## Using Machine Learning in Trading and Finance
+
+- Issuer: Google
+- Issued: Nov 2020
+- Credential ID: X5YYLBMPY4BU
+
+## DeepLearning.AI TensorFlow Developer Specialization
+
+- Issuer: DeepLearning.AI
+- Issued: Oct 2020
+- Credential ID: LQ4GHWJ6URBS
+
+## Perform Foundational Data, ML, and AI Tasks in Google Cloud
+
+- Issuer: Google
+- Issued: Oct 2020
+
+## Professional Machine Learning Engineer
+
+- Issuer: Google
+- Issued: Oct 2020
+- Credential ID: 24896478
+
+## Sequences, Time Series and Prediction
+
+- Issuer: Google
+- Issued: Oct 2020
+- Credential ID: WHBV68C4WJT5
+
+## Convolutional Neural Networks in TensorFlow
+
+- Issuer: Google
+- Issued: Sep 2020
+- Credential ID: 78HJEJZ3T2BB
+
+## Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
+
+- Issuer: Google
+- Issued: Sep 2020
+- Credential ID: SW885ZMDHTYM
+
+## Natural Language Processing in TensorFlow
+
+- Issuer: Google
+- Issued: Sep 2020
+- Credential ID: JZ9TBHXJFLWM
+
+## Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization
+
+- Issuer: Google
+- Issued: Jul 2020
+- Credential ID: V492QQ4JJKEB
+
+## End-to-End Machine Learning with TensorFlow on GCP
+
+- Issuer: Google
+- Issued: Jul 2020
+- Credential ID: QLDMNADDBSRR
+
+## Image Understanding with TensorFlow on GCP
+
+- Issuer: Google
+- Issued: Jul 2020
+- Credential ID: HY4HSSY8JSPN
+
+## Production Machine Learning Systems
+
+- Issuer: Google
+- Issued: Jul 2020
+- Credential ID: THZZNW22LHKT
+
+## Recommendation Systems with TensorFlow on GCP
+
+- Issuer: Google
+- Issued: Jul 2020
+- Credential ID: 2D4LT28697TC
+
+## Sequence Models for Time Series and Natural Language Processing
+
+- Issuer: Google
+- Issued: Jul 2020
+- Credential ID: 6XUV7YJFM3ZA
+
+## Building Batch Data Pipelines on GCP
+
+- Issuer: Google
+- Issued: May 2020
+- Credential ID: 5QYSK9E5EAFN
+
+## Building Resilient Streaming Analytics Systems on GCP
+
+- Issuer: Google
+- Issued: May 2020
+- Credential ID: FYQW7D4F6PD4
+
+## Data Engineering with Google Cloud Specialization
+
+- Issuer: Google
+- Issued: May 2020
+- Credential ID: EPZ3WQFC423E
+
+## Modernizing Data Lakes and Data Warehouses with GCP
+
+- Issuer: Google
+- Issued: May 2020
+- Credential ID: 393P3HLZWY8H
+
+## Smart Analytics, Machine Learning, and AI on GCP
+
+- Issuer: Google
+- Issued: May 2020
+- Credential ID: AK77VUVN4ARJ
+
+## Google Cloud Platform Big Data and Machine Learning Fundamentals
+
+- Issuer: Google
+- Issued: Apr 2020
+- Credential ID: 2Q35NYHYMW5E
+
+## Devenez Mentor Evaluateur
+
+- Issuer: OpenClassrooms
+- Issued: Feb 2019
+- Credential ID: 8151214336
+
+## Advanced AI: Deep Reinforcement Learning in Python
+
+- Issuer: Udemy
+- Issued: Aug 2018
+- Credential ID: UC-5FM0CC9S
+
+## Artificial Intelligence: Reinforcement Learning in Python
+
+- Issuer: Udemy
+- Issued: Jul 2018
+- Credential ID: UC-XALJEH7G
+
+## Concevez un site avec Flask
+
+- Issuer: OpenClassrooms
+- Issued: Jul 2018
+- Credential ID: 5343531703
+
+## Les étapes de la vie du Mentor
+
+- Issuer: OpenClassrooms
+- Issued: Jul 2018
+- Credential ID: 8431716200
+
+## Devenez Mentor chez OpenClassrooms
+
+- Issuer: OpenClassrooms
+- Issued: May 2018
+- Credential ID: 6193593386
+
+## Complete Guide to ElasticSearch
+
+- Issuer: Udemy
+- Issued: Mar 2018
+- Credential ID: UC-H5AJQVA3
+
+## Introduction to Hadoop
+
+- Issuer: The Linux Foundation
+- Issued: Oct 2017
+- Credential ID: ad676a8fe7994edea33516b80b540971
+
+## Artificial Intelligence Nanodegree
+
+- Issuer: Udacity
+- Issued: Sep 2017
+- Credential ID: PV7A7EAA
+
+## High Performance Computing
+
+- Issuer: University of Luxembourg
+- Issued: Feb 2017
+
+## Machine Learning
+
+- Issuer: Standford University
+- Issued: Sep 2015
+- Grade: 97%
+
+## TOEIC
+
+- Skills: Listening, Reading
+- Issued: Jan 2014
+- Score: 975/990
+
+# Publications
+
+## Is AI/ML Monitoring just Data Engineering? 🤔
+
+- Publisher: MLOps Community
+- Publication date: July 24, 2023
+- Link: https://mlops.community/is-ai-ml-monitoring-just-data-engineering-%f0%9f%a4%94/
+
+While the future of machine learning and MLOps is being debated,
+practitioners still need to attend to their machine learning models
+in production. This is no easy task, as ML engineers must constantly
+assess the quality of the data that enters and exits their
+pipelines, and ensure that their models generate the correct
+predictions. To assist ML engineers with this challenge, several
+AI/ML monitoring solutions have been developed.
+In this article, I will discuss the nature of AI/ML monitoring and
+how it relates to data engineering. First, I will present the
+similarities between AI/ML monitoring and data engineering. Second,
+I will enumerate additional features that AI/ML monitoring solutions
+can provide. Third, I will briefly touch on the topic of AI/ML
+observability and its relation to AI/ML monitoring. Finally, I will
+provide my conclusion about the field of AI/ML monitoring and how it
+should be considered to ensure the success of your AI/ML project.
+
+## A great MLOps project should start with a good Python Package 🐍
+
+- Publisher: MLOps Community
+- Publication date: June 28, 2023
+- Link: https://mlops.community/a-great-mlops-project-should-start-with-a-good-python-package-%f0%9f%90%8d/
+
+In this article, I present the implementation of a Python package on
+GitHub designed to support MLOps initiatives. The goal of this
+package is to make the coding workflow of data scientists and ML
+engineers as flexible, robust, and productive as possible. First, I
+start by motivating the use of Python packages. Then, I provide some
+tools and tips you can include in your MLOps project. Finally, I
+explain the follow-up steps required to take this package to the
+next level and make it work in your environment.
+
+## Fixing the MLOps Survey on LLMs with ChatGPT API: Lessons Learned
+
+- Publisher: MLOps Community
+- Publication date: May 11, 2023
+- Link: https://mlops.community/fixing-the-mlops-survey-on-llms-with-chatgpt-api-lessons-learned/
+
+Large Language Model (LLM) is such an existing topic. Since the
+release of ChatGPT, we saw a surge of innovation ranging from
+education mentorship to finance advisory. Each week is a new
+opportunity for addressing new kinds of problems, increasing human
+productivity, or improving existing solutions. Yet, we may wonder if
+this is just a new hype cycle or if organizations are truly adopting
+LLMs at scale …
+On March 2023, the MLOps Community issued a survey about LLMs in
+production to picture the state of adoption. The survey is full of
+interesting insights, but there is a catch: 80% of the questions are
+open-ended, which means respondents answered the survey freely from
+a few keywords to full sentences. I volunteered to clean up the
+answers with the help of ChatGPT and let the community get a grasp
+of the survey experiences.
+In this article, I present the steps and lessons learned from my
+journey to shed some light on the MLOps survey on LLMs. I’m first
+going to present the goal and questions of the survey. Then, I will
+explain how I used ChatGPT to review the data and standardize the
+content. Finally, I’m going to evaluate the performance of ChatGPT
+compared to a manual review.
+
+## Kubeflow: The Machine Learning Toolkit for Kubernetes
+
+- Publisher: MLOps Community in Luxembourg
+- Publication date: April 26, 2023
+- Link: https://docs.google.com/presentation/d/1WtN5mNAHsz7PiWE5kKZUQfQ9_zO_kfVoSBNTHQM-sMk/edit#slide=id.p1
+
+## MLflow: An open source platform for the machine learning lifecycle
+
+- Publisher: MLOps Community in Luxembourg
+- Publication date: April 26, 2023
+- Link: https://docs.google.com/presentation/d/1fjoQ2DnTzhyaMI9dC6E2_RIKylW8hAGS5iQ6TapZfZo/edit#slide=id.g1e1b3ce30cd_0_0
+
+## We need POSIX for MLOps
+
+- Publisher: MLOps Community
+- Publication date: April 21, 2023
+- Link: https://mlops.community/we-need-posix-for-mlops/
+
+If you work on MLOps, you must navigate an ever-growing landscape of
+tools and solutions. This is both an intense source of stimulation
+and fatigue for MLOps practitioners.
+Vendors and users face the same problem: How can we combine all
+these tools without the combinatorial complexity of creating custom
+integrations?
+In this article, I propose a solution analogous to POSIX to address
+this challenge. First, I motivate the creation of common protocols
+and schemas for combining MLOps tools. Second, I present a
+high-level architecture to support implementation. Third, I conclude
+with the benefits and limitations of standardizing MLOps.
+
+## How to install Kubeflow Pipelines v2 on Apple Silicon
+
+- Publisher: Medium
+- Publication date: September 24, 2022
+- Link: https://fmind.medium.com/how-to-install-kubeflow-on-apple-silicon-3565db8773f3
+
+Kubeflow Pipelines (KFP) is a powerful platform for building machine
+learning pipelines at scale with Kubernetes. The platform is well
+supported on major cloud platforms such as GCP (Vertex AI Pipelines)
+or AWS (Kubeflow on AWS). However, installing KFP on Apple Silicon
+(macOS 12.5.1 with Apple M1 Pro) proved to be more challenging than
+I imagined. Thus, I wanted to share my experience and tips to
+install KFP as easily as possible on your shiny Mac.
+In this article, I present 4 steps to install Kubeflow on Apple
+Silicon, using Rancher Desktop for setting up Docker/Kubernetes. In
+the end, I list the problems I encountered during the installation
+of Kubeflow Pipelines.
+
+## The Programming Trade-Off: Purpose, Productivity, Performance
+
+- Publisher: Medium
+- Publication: August 15, 2019
+- Link: https://fmind.medium.com/3p-principle-purpose-productivity-performance-630bed7623fc
+
+As programmers, we are continuously looking for languages that are
+performant, productive, and general purpose. Is there any
+programming language that currently satisfies these properties? Can
+we ever create one?
+In this article, I present a fundamental trade-off that affects the
+design of programming languages and the success of software
+projects.
+
+## Creating better ground truth to further understand Android malware: A large scale mining approach based on antivirus labels and malicious artifacts
+
+- Publisher: University of Luxembourg
+- Publication date: July 1, 2019
+- Link: https://orbilu.uni.lu/handle/10993/39903
+
+Mobile applications are essential for interacting with technology
+and other people. With more than 2 billion devices deployed all over
+the world, Android offers a thriving ecosystem by making accessible
+the work of thousands of developers on digital marketplaces such as
+Google Play. Nevertheless, the success of Android also exposes
+millions of users to malware authors who seek to siphon private
+information and hijack mobile devices for their benefits.
+To fight against the proliferation of Android malware, the security
+community embraced machine learning, a branch of artificial
+intelligence that powers a new generation of detection systems.
+Machine learning algorithms, however, require a substantial number
+of qualified samples to learn the classification rules enforced by
+security experts. Unfortunately, malware ground truths are
+notoriously hard to construct due to the inherent complexity of
+Android applications and the global lack of public information about
+malware. In a context where both information and human resources are
+limited, the security community is in demand for new approaches to
+aid practitioners to accurately define Android malware, automate
+classification decisions, and improve the comprehension of Android
+malware.
+This dissertation proposes three solutions to assist with the
+creation of malware ground truths.
+
+## Euphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
+
+- Publisher: MSR 2017
+- Publication date: May 21, 2017
+- Link: https://orbilu.uni.lu/handle/10993/31441
+
+Android malware is now pervasive and evolving rapidly. Thousands of
+malware samples are discovered every day with new models of attacks.
+The growth of these threats has come hand in hand with the
+proliferation of collective repositories sharing the latest
+specimens. Having access to a large number of samples opens new
+research directions aiming at efficiently vetting apps. However,
+automatically inferring a reference ground-truth from those
+repositories is not straightforward and can inadvertently lead to
+unforeseen misconceptions. On the one hand, samples are often
+mislabeled as different parties use distinct naming schemes for the
+same sample. On the other hand, samples are frequently misclassified
+due to conceptual errors made during labeling processes.
+In this paper, we analyze the associations between all labels given
+by different vendors and we propose a system called EUPHONY to
+systematically unify common samples into family groups. The key
+novelty of our approach is that no prior knowledge of malware
+families is needed. We evaluate our approach using reference
+datasets and more than 0.4 million additional samples outside of
+these datasets. Results show that EUPHONY provides competitive
+performance against the state-of-the-art.
+
+## On the Lack of Consensus in Anti-Virus Decisions: Metrics and Insights on Building Ground Truths of Android Malware
+
+- Publisher: DIMVA 2016
+- Publication date: July 7, 2016
+- Link: https://orbilu.uni.lu/handle/10993/27845
+
+There is generally a lack of consensus in Antivirus (AV) engines'
+decisions on a given sample. This challenges the building of
+authoritative ground-truth datasets. Instead, researchers and
+practitioners may rely on unvalidated approaches to build their
+ground truth, e.g., by considering decisions from a selected set of
+Antivirus vendors or by setting up a threshold number of positive
+detections before classifying a sample. Both approaches are biased
+as they implicitly either decide on ranking AV products, or they
+consider that all AV decisions have equal weights. In this paper, we
+extensively investigate the lack of agreement among AV engines.
+To that end, we propose a set of metrics that quantitatively
+describe the different dimensions of this lack of consensus. We show
+how our metrics can bring important insights by using the detection
+results of 66 AV products on 2 million Android apps as a case study.
+Our analysis focuses not only on AV binary decision but also on the
+notoriously hard problem of labels that AVs associate with
+suspicious files, and allows to highlight biases hidden in the
+collection of a malware ground truth---a foundation stone of any
+machine learning-based malware detection approach.
+
+# Projects
+
+## MLOps Python Package
+
+- Date: Jun 2023 - Jun 2023
+- Description: Kickstart your MLOps initiative with a flexible, robust, and productive Python package.
+- Link: https://github.com/fmind/mlops-python-package
+
+## Fixing the MLOps Survey with ChatGPT
+
+- Date: May 2023 - May 2023
+- Description: Fixing the MLOps Survey on LLMs with ChatGPT API.
+- Link: https://fmind.medium.com/fixing-the-mlops-survey-on-llms-with-chatgpt-api-lessons-learned-62d90e721331
+
+## Kubeflow Demo
+
+- Date: Apr 2023 - Apr 2023
+- Description: Kubeflow demo for the MLOps Community Meetup in Luxembourg.
+- Link: https://github.com/fmind/kubeflow-demo
+
+## MLflow Demo
+
+- Date: Apr 2023 - Apr 2023
+- Description: MLflow demo for the MLOps Community Meetup in Luxembourg.
+- Link: https://github.com/fmind/mlflow-demo
+
+## onet
+
+- Date: Aug 2020 - Sep 2020
+- Description: Train and predict procedures of DNN for binary image classification
+- Link: https://github.com/fmind/onet
+
+## fincrawl
+
+- Date: Nov 2019 - Dec 2019
+- Description: Crawl documents, metadata, and files from financial institutions
+- Link: https://github.com/fmind/fincrawl
+
+## invest
+
+- Date: Aug 2019 - Sep 2019
+- Description: Stock market analysis focused on dividends
+- Link: https://github.com/fmind/invest
+
+## parsoc
+
+- Date: Jul 2019 - Sep 2019
+- Description: Convert docx files to json
+- Link: https://github.com/fmind/parsoc
+
+## Bigdata Tutorials
+
+- Date: Sep 2015 - Jul 2019
+- Description: Tutorials for the Big Data course @ uni.lu
+- Link: https://github.com/fmind/bigdata-tutorials
+
+## STASE: A set of statistical metrics to better understand and qualify malware datasets
+
+- Date: Apr 2016 - Jul 2019
+- Description: A handful of statistical metrics to better understand and qualify malware datasets
+- Link: https://github.com/fmind/STASE
+
+## apkworkers
+
+- Date: Sep 2015 - Jul 2019
+- Description: A celery application to distribute Android malware analysis
+- Link: https://github.com/fmind/apkworkers
+
+## servalx
+
+- Date: Sep 2015 - Jul 2019
+- Description: A set of tools and modules to process Android malware with Androzoo
+- Link: https://github.com/fmind/servalx
+
+## Euphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
+
+- Date: Mar 2017 - Mar 2019
+- Description: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
+- Link: https://github.com/fmind/euphony
+
+## Automatic Speech Recognition with Tensorflow
+
+- Date: Sep 2017 - Sep 2017
+- Description: An automatic speech-recognition system based on Tensorflow
+- Link: https://github.com/fmind/AIND-VUI-Capstone
+
+## Dog Recognition with Tensorflow
+
+- Date: Aug 2017 - Aug 2017
+- Description: A machine-learning model train to recognize dogs, even from human faces
+- Link: https://github.com/fmind/dog-project
+
+## genius
+
+- Date: Jun 2017 - Jul 2017
+- Description: An implementation of LISP Scheme based on Haskell
+- Link: https://github.com/fmind/genius
+
+## Alexa History Skill
+
+- Date: Jun 2017 - Jun 2017
+- Description: A Alexa skill that provides year-dated facts on demand
+- Link: https://github.com/fmind/AIND-VUI-Alexa
+
+## Air Cargo Planning System
+
+- Date: Feb 2017 - Apr 2017
+- Description: An automated Air Cargo transport system based on AI planning
+- Link: https://github.com/fmind/AIND-Planning
+
+## Sign Language Recognition System
+
+- Date: Feb 2017 - Apr 2017
+- Description: A sign recognition system based on Hidden Markov Model
+- Link: https://github.com/fmind/AIND-Recognizer
+
+## AI Agent for the Isolation Game
+
+- Date: Mar 2017 - Mar 2017
+- Description: An AI game agent to play the Isolation game
+- Link: https://github.com/fmind/AIND-Isolation
+
+## Sudoku Solver
+
+- Date: Jan 2017 - Feb 2017
+- Description: A Diagonal Sudoku solver implemented with Python
+- Link: https://github.com/fmind/AIND-Sudoku
+
+## lkml
+
+- Date: Nov 2016 - Jan 2017
+- Description: Gather emails from https://lkml.org/
+- Link: https://github.com/fmind/lkml
+
+## Master 2 School Projects
+
+- Date: Sep 2013 - Jun 2014
+- Description: School projects from 2013 to 2014 - Master 2 Sécurité des Systèmes d'Information (Metz)
+- Link: https://github.com/fmind/master2-projects
+
+## chattail
+
+- Date: Dec 2013 - Mar 2014
+- Description: Send log streams over XMPP to monitor your systems
+- Link: https://github.com/fmind/chattail
+
+## Master 1 School Projects
+
+- Date: Jun 2012 - Sep 2013
+- Link: https://github.com/fmind/master1-projects
+
+## Bachelor School Projects
+
+- Date: Jun 2011 - Sep 2012
+- Link: https://github.com/fmind/bachelor-projects
+
+## Professional Bachelor School Project
+
+- Date: Sep 2009 - Jun 2010
+- Link: https://github.com/fmind/professional-bachelor-project
+
+# Skills
+
+## Artificial Intelligence / Machine Learning
+
+- Artificial Intelligence (AI)
+- Machine Learning
+- MLOps
+- Deep Learning
+- Data Science
+- Statistics
+- ChatGPT
+- Scikit-Learn
+- Tensorflow
+- KubeFlow
+- MLflow
+- Jupyter
+- Pandas
+- Keras
+- DVC
+- Transformers
+- Natural Language Processing (NLP)
+
+## Software Engineering
+
+- Python
+- Functional Programming (FP)
+- Object-Oriented Programming (OOP)
+- API REST
+- Android
+- Docker
+- JSON
+- Git
+
+## Cloud Platforms
+
+- AWS SageMaker
+- Vertex AI (GCP)
+- Google Cloud Platform (GCP)
+- Microsoft Azure Machine Learning
+- Apache Airflow
+- Kubernetes
+- DataBricks
+- Terraform
+- Ansible
+- Linux
+
+## Computer Security
+
+- ISO 27001
+- Cybersecurity
+
+## Data Management
+
+- NoSQL
+- MongoDB
+- Big Data
+- PostgreSQL
+- Apache Spark
+- ElasticSearch
+- Data Engineering
+
+## Project Management
+
+- Agile Methodology
+- Project Management
+- Jira
+- UML
+
+# Languages
+
+## Français
+
+- Proficiency: Native or bilingual proficiency
+
+## English
+
+- Proficiency: Full professional proficiency

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+Médéric HURIER
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+Freelancer: AI/ML/MLOps Engineer | Data Scientist | MLOps Community Organizer | OpenClassrooms Mentor | Hacker | PhD
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+Decathlon Technology
+
+University of Luxembourg
+
+Websites
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+Websites
+
+Company Website
+     https://www.fmind.dev
+
+Portfolio
+     https://github.com/fmind/
+
+Blog
+     https://fmind.medium.com/
+
+-   Report this profile
+
+About
+
+Note: I'm not available to work on new missions until the 1st of
+September 2023. Thank you for your understanding.
+When I worked as a teacher, I told my students that Artificial
+Intelligence and Machine Learning are the most effective levers to make
+a difference. Every day, new AI and ML solutions are released to empower
+companies and individuals alike. The question is: Is your business ready
+to provide the best AI/ML products for your customers?
+I'm a professional Machine Learning Engineer, Data Scientist, and MLOps
+ready to assist you in this quest. I've completed a Ph.D. in Machine
+Learning and several high-end AI/ML certifications to help you build
+leading data-driven services. My past experiences include working with
+companies like Google, BNP Paribas, ArcelorMittal, the European
+Commission, and Decathlon to frame their needs, create state-of-the-art
+models and deliver AI/ML artifacts at scale.
+I now work as a freelancer in Luxembourg, and I can carry out missions
+remotely in other European countries. You can get in touch with me on
+LinkedIn or at [email protected]. I'll be happy to collaborate with you
+or discuss your favored AI/ML topics in the MLOps Community.
+
+Experience
+
+-   
+    Lead MLOps Engineer
+
+    Decathlon Technology
+
+    Sep 2022 - Present1 year 2 months
+
+    Luxembourg
+
+    Continue the design and implementation of Decathlon's MLOps platform
+    with Databricks and AWS.
+
+-   
+    Mentor for aspiring Data Scientist and AI/ML Engineer
+
+    OpenClassrooms
+
+    Mar 2023 - Present8 months
+
+    France
+
+    Tutoring adult students to become data scientists specializing in
+    machine learning.
+    - https://openclassrooms.com/fr/paths/793-data-scientist
+    - https://openclassrooms.com/fr/paths/794-machine-learning-engineer
+    - https://openclassrooms.com/fr/paths/795-ai-engineer
+
+-   
+    Senior Data Scientist & Project Manager for the European Commission
+
+    Cronos Europa
+
+    Dec 2021 - Sep 202210 months
+
+    Luxembourg
+
+    Mission: Enhance the ARACHNE risk scoring tool (fraud detection).
+    Main tasks and responsibilities:
+    - Develop a new version of Arachne using data mining techniques
+    - Manage the development of the Arachne PoC/Project (SCRUM)
+    - Assist data scientists in their projects (Virtual Assistant, NLP,
+    …)
+    Technical stack:
+    - Data Science: Python, PostgreSQL, SQLAlchemy, Hugging Face,
+    HayStack
+    - Management/Environment: Jira, Confluence, MS Office, AWS, Azure
+
+-   
+
+    SFEIR
+
+    2 years
+
+    -   
+        Project Manager & Machine Learning Engineer for Decathlon
+
+        SFEIR
+
+        Dec 2020 - Dec 20211 year 1 month
+
+        Luxembourg
+
+        Mission: Design and implement the next ML/MLOps platform on AWS
+        and GCP.
+        Main tasks and responsibilities:
+        - Design the functional & technical architecture of the platform
+        - Manage the MLOps@Decathlon initiative (tasks, plannings)
+        - Select the vendor solutions based on a user need analysis
+        - Communicate the progress and success to stack-holders
+        - Assist data scientists in their project (audience, forecast)
+        Technical stack:
+        - Data Science: Python, TensorFlow… Show more
+
+        Mission: Design and implement the next ML/MLOps platform on AWS
+        and GCP.
+        Main tasks and responsibilities:
+        - Design the functional & technical architecture of the platform
+        - Manage the MLOps@Decathlon initiative (tasks, plannings)
+        - Select the vendor solutions based on a user need analysis
+        - Communicate the progress and success to stack-holders
+        - Assist data scientists in their project (audience, forecast)
+        Technical stack:
+        - Data Science: Python, TensorFlow, Spark, sklearn, Jupyter,
+        Airflow
+        - Management: Google Workspace, Jira, UML, Terraform, Jenkins
+        - Environments: AWS (SageMaker), GCP (Vertex AI), DataBricks
+        Show less
+    -   
+        Data Scientist for SFEIR
+
+        SFEIR
+
+        Oct 2020 - Nov 20202 months
+
+        Luxembourg, Luxembourg
+
+        Mission: Improve the visibility and assets of SFEIR's Data Team.
+        Main tasks and responsibilities:
+        - Design and create technical interviews for recruiting data
+        scientists.
+        - Become a Professional Machine Learning Engineer on Google
+        Cloud.
+        - Propose a strategy to improve the online visibility of SFEIR
+        data team.
+        - Share knowledge about data trends with non-technical staff
+        members.
+        - Create a group to write tutorials and kata on AI/ML for SFEIR
+        developers.
+    -   
+        Data Scientist for ArcelorMittal
+
+        SFEIR
+
+        Jan 2020 - Sep 20209 months
+
+        Luxembourg
+
+        Mission: Train and optimize machine learning models to recommend
+        steel prices.
+        Main tasks and responsibilities:
+        - Create and fine-tune machine-learning models (tree-based)
+        - Evaluate the performance of the model on real datasets
+        - Communicate the results to business stack-holders
+        Technical stack:
+        - Data Science: Python, XGBoost, sklearn, Jupyter, SQL
+        - Analytics: Matplotlib, Seaborn, Tableau, Plotly, Dash
+        - Environment: MS-SQL, Azure Cloud, Jira, Papermill
+
+-   
+
+    University of Luxembourg
+
+    4 years 5 months
+
+    -   
+        Research And Development Specialist
+
+        University of Luxembourg
+
+        Sep 2019 - Jan 20205 months
+
+        Luxembourg, Luxembourg
+
+        Management and development of Natural Language Understanding
+        (NLU) projects for BGL BNP Paribas.
+    -   
+        Doctoral researcher
+
+        University of Luxembourg
+
+        Sep 2015 - Jan 20204 years 5 months
+
+        Luxembourg, Luxembourg
+
+        Research activities focused on Android security and artificial
+        intelligence.
+        Teaching big data, machine learning and Android programming to
+        students.
+        Collaboration with Google, San Francisco on finding malicious
+        Android artifacts.
+
+-   
+    Mentor for aspiring Data Scientist
+
+    OpenClassrooms
+
+    Aug 2018 - Dec 20191 year 5 months
+
+    France
+
+    Tutoring adult students to become data scientists specializing in
+    machine learning.
+
+-   
+    Security engineer specialized in log management and analysis
+
+    Clearstream
+
+    Apr 2014 - Aug 20151 year 5 months
+
+    Luxembourg, Luxembourg
+
+    Selection and deployment of a SIEM solution, participating in
+    security incident response.
+
+-   Web developer and administrator
+
+    Freaxmind
+
+    Aug 2011 - Aug 20132 years 1 month
+
+    France
+
+    Various contracts ranging from web development to software
+    maintenance and debugging.
+
+-   
+    Web Developer
+
+    Toul'embal
+
+    Jun 2012 - Aug 20123 months
+
+    Toul, France
+
+    Extension of a Prestashop e-commerce website and creation a
+    portfolio website with WordPress.
+
+-   Web Programmer
+
+    Empreinte Studio
+
+    Oct 2010 - Aug 201111 months
+
+    Épernay, France
+
+    Creation of modern website in PHP and MySQL with professional
+    writers and graphic designers.
+
+-   
+    Software Developer
+
+    GEOVARIANCES
+
+    Sep 2009 - Sep 20101 year 1 month
+
+    Avon, France
+
+    Development of a geostatistic application in C++ and Qt with
+    experienced software engineers.
+
+-   
+    Web Developer
+
+    CV Champagne Nicolas Feuillatte
+
+    Apr 2009 - Aug 20095 months
+
+    Épernay, France
+
+    Integration of customer and share management modules to J.D. Edwards
+    with PHP and Oracle.
+
+Education
+
+-   
+    University of Luxembourg
+
+    Doctor of Philosophy - PhDComputer Security and Artificial IntelligenceVery Good
+
+    2015 - 2019
+
+    Activities and Societies: Teach Big Data and Android to students.
+
+    Thesis title: Creating better ground truth to further understand
+    Android malware
+
+-   UFR Mathématiques, Informatique, Mécanique et Automatique, Metz (France)
+
+    Master's degreecomputer and information systems security
+
+    2013 - 2014
+
+-   UFR Mathématiques et Informatique de l’Université de Lorraine, Nancy (France)
+
+    Bachelor and master yearscomputer science applied to business informatics
+
+    2011 - 2013
+
+-   IUT Sénart-Fontainebleau, Fontainebleau (France)
+
+    Professional bachelor's degreecomputer security and databases
+
+    2009 - 2010
+
+-   IUT Nancy-Charlemagne, Nancy (France)
+
+    Professional bachelor’s degreeweb development and integration
+
+    2008 - 2009
+
+-   Lycée François 1er, Vitry-le-François (France)
+
+    Technical degreenetwork and software development
+
+    2006 - 2008
+
+-   Lycée Marc Chagall, Reims (France)
+
+    Baccalauréat général degreescience, specialized in biology
+
+    2003 - 2006
+
+Volunteer Experience
+
+-    MLOps Community Organizer (Luxembourg)
+    MLOps Community Organizer (Luxembourg)
+
+    MLOps Community
+
+    Nov 2022
+
+    Science and Technology
+
+    Organize regular meetups and events for the MLOps Community.
+    Regular partnership with AWS and the University of Luxembourg.
+    https://www.meetup.com/luxembourg-mlops-community/
+
+Licenses & Certifications
+
+-   Machine Learning Associate
+
+    Databricks
+
+    Issued Nov 2022
+
+    Credential ID 61461287
+
+    See credential
+
+-   Databricks Lakehouse Fundamentals
+
+    Databricks
+
+    Issued Oct 2022
+
+    Credential ID 61029028
+
+    See credential
+
+-   Architecting with Google Kubernetes Engine Specialization
+
+    Google
+
+    Issued Sep 2022
+
+    Credential ID WLU4DBPSQ4B5
+
+    See credential
+
+-   Architecting with Google Kubernetes Engine: Foundations
+
+    Google
+
+    Issued Sep 2022
+
+    Credential ID DFWAC6BXLNGL
+
+    See credential
+
+-   Architecting with Google Kubernetes Engine: Production
+
+    Google
+
+    Issued Sep 2022
+
+    Credential ID K5SZHUST5HP2
+
+    See credential
+
+-   Architecting with Google Kubernetes Engine: Workloads
+
+    Google
+
+    Issued Sep 2022
+
+    Credential ID ULJQAXGDVKYK
+
+    See credential
+
+-   Google Cloud Fundamentals: Core Infrastructure
+
+    Google
+
+    Issued Sep 2022
+
+    Credential ID 4CE8WQ6AWKFF
+
+    See credential
+
+-   Iterative Tools for Data Scientists and Analysts
+
+    Iterative
+
+    Issued Aug 2022
+
+    Credential ID 62fcb79418f51945ea
+
+    See credential
+
+-   Azure Data Scientist Associate
+
+    Microsoft
+
+    Issued Jul 2022
+
+    Credential ID 992564946
+
+    See credential
+
+-   Azure Machine Learning for Data Scientists
+
+    Microsoft
+
+    Issued Jun 2022
+
+    Credential ID MZKV7LSTQ9HX
+
+    See credential
+
+-   Build and Operate Machine Learning Solutions with Azure Microsoft
+
+    Microsoft
+
+    Issued Jun 2022
+
+    Credential ID 7FBX68MH272C
+
+    See credential
+
+-   Create Machine Learning Models in Microsoft Azure
+
+    Microsoft
+
+    Issued Jun 2022
+
+    Credential ID SHALM9PM3MPX
+
+    See credential
+
+-   Microsoft Azure Data Scientist Associate - DP-100 Test Prep Specialization
+
+    Microsoft
+
+    Issued Jun 2022
+
+    Credential ID L5P3TYLAYLLT
+
+    See credential
+
+-   Perform data science with Azure Databricks
+
+    Microsoft
+
+    Issued Jun 2022
+
+    Credential ID RQ7PLFYZVLXX
+
+    See credential
+
+-   Prepare for DP-100: Data Science on Microsoft Azure Exam
+
+    Microsoft
+
+    Issued Jun 2022
+
+    Credential ID K5KW27AVMYS2
+
+    See credential
+
+-   Neo4j Graph Data Science Certified
+
+    Neo4j
+
+    Issued Apr 2022
+
+    Credential ID 17351346
+
+    See credential
+
+-   Microsoft Certified: Azure AI Fundamentals
+
+    Microsoft
+
+    Issued Jan 2022
+
+    Credential ID 1098-0884
+
+    See credential
+
+-   Artificial Intelligence on Microsoft Azure
+
+    Microsoft
+
+    Issued Dec 2021
+
+    Credential ID Z8FSWXBSAGLD
+
+    See credential
+
+-   Computer Vision in Microsoft Azure
+
+    Microsoft
+
+    Issued Dec 2021
+
+    Credential ID KDDPYLKM2DA5
+
+    See credential
+
+-   Microsoft Azure AI Fundamentals AI-900 Exam Prep Specialization
+
+    Microsoft
+
+    Issued Dec 2021
+
+    Credential ID 96944QKZH9BU
+
+    See credential
+
+-   Microsoft Azure Machine Learning
+
+    Microsoft
+
+    Issued Dec 2021
+
+    Credential ID 32ES25845Q55
+
+    See credential
+
+-   Natural Language Processing in Microsoft Azure
+
+    Microsoft
+
+    Issued Dec 2021
+
+    Credential ID XVN23N8CKRGY
+
+    See credential
+
+-   Preparing for AI-900: Microsoft Azure AI Fundamentals exam
+
+    Microsoft
+
+    Issued Dec 2021
+
+    Credential ID YC83C22L8TBL
+
+    See credential
+
+-   Build a Website on Google Cloud
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Build and Secure Networks in Google Cloud
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Create ML Models with BigQuery ML
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Create and Manage Cloud Resources
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Deploy to Kubernetes in Google Cloud
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Implement DevOps in Google Cloud
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Insights from Data with BigQuery
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Integrate with Machine Learning APIs
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Perform Foundational Infrastructure Tasks in Google Cloud
+
+    Google
+
+    Issued Aug 2021
+
+    See credential
+
+-   Apache Spark Associate Developer
+
+    Databricks
+
+    Issued Jun 2021
+
+    Credential ID fff03919-bbc9-304e-99ad-6f2ed47455ed
+
+    See credential
+
+-   Scalable Machine Learning with Apache Spark
+
+    Databricks
+
+    Issued May 2021
+
+    Credential ID 0f4adf96-0412-32f2-8232-fa50c51c9b47
+
+    See credential
+
+-   Apache Spark Programming with Databricks
+
+    Databricks
+
+    Issued May 2021
+
+    Credential ID 518a1d63-8894-3ab5-aaa5-50a9f169436c
+
+    See credential
+
+-   Data Science Professional
+
+    Databricks
+
+    Issued May 2021
+
+    Credential ID f05164e1-5a78-37f8-9c69-3e996fdbb21f
+
+    See credential
+
+-   Delta Lake Fundamentals Accreditation
+
+    Databricks
+
+    Issued May 2021
+
+    Credential ID 0d042e3f-50d3-3821-b064-f3c12ca6c17f
+
+    See credential
+
+-   Deploying a Machine Learning Project with MLflow Projects
+
+    Databricks
+
+    Issued May 2021
+
+    Credential ID 2afa0c7f-48f4-35af-b366-f7c77d2cd20a
+
+    See credential
+
+-   Tracking Experiments with MLflow
+
+    Databricks
+
+    Issued May 2021
+
+    Credential ID 0cbf87b7-e096-3792-a3b7-62d86aa6380d
+
+    See credential
+
+-   Unified Data Analytics Accreditation
+
+    Databricks
+
+    Issued May 2021
+
+    Credential ID afba5402-b5e4-3f9e-95f2-51d6bbb5fa64
+
+    See credential
+
+-   ML Pipelines on Google Cloud
+
+    Google
+
+    Issued Mar 2021
+
+    Credential ID FN5PYWX5PRCP
+
+    See credential
+
+-   Introduction to Trading, Machine Learning & GCP
+
+    Google
+
+    Issued Nov 2020
+
+    Credential ID YV9H5PF4YPLZ
+
+    See credential
+
+-   MLOps (Machine Learning Operations) Fundamentals
+
+    Google
+
+    Issued Nov 2020
+
+    Credential ID 4BDA24UL7K9Z
+
+    See credential
+
+-   Machine Learning for Trading Specialization
+
+    Google
+
+    Issued Nov 2020
+
+    Credential ID YSNPABSMV6JL
+
+    See credential
+
+-   Reinforcement Learning for Trading Strategies
+
+    Google
+
+    Issued Nov 2020
+
+    Credential ID VHKJLFPLLDLU
+
+    See credential
+
+-   Using Machine Learning in Trading and Finance
+
+    Google
+
+    Issued Nov 2020
+
+    Credential ID X5YYLBMPY4BU
+
+    See credential
+
+-   DeepLearning.AI TensorFlow Developer Specialization
+
+    DeepLearning.AI
+
+    Issued Oct 2020
+
+    Credential ID LQ4GHWJ6URBS
+
+    See credential
+
+-   Perform Foundational Data, ML, and AI Tasks in Google Cloud
+
+    Google
+
+    Issued Oct 2020
+
+    See credential
+
+-   Professional Machine Learning Engineer
+
+    Google
+
+    Issued Oct 2020
+
+    Credential ID 24896478
+
+    See credential
+
+-   Sequences, Time Series and Prediction
+
+    Google
+
+    Issued Oct 2020
+
+    Credential ID WHBV68C4WJT5
+
+    See credential
+
+-   Convolutional Neural Networks in TensorFlow
+
+    Google
+
+    Issued Sep 2020
+
+    Credential ID 78HJEJZ3T2BB
+
+    See credential
+
+-   Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
+
+    Google
+
+    Issued Sep 2020
+
+    Credential ID SW885ZMDHTYM
+
+    See credential
+
+-   Natural Language Processing in TensorFlow
+
+    Google
+
+    Issued Sep 2020
+
+    Credential ID JZ9TBHXJFLWM
+
+    See credential
+
+-   Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization
+
+    Google
+
+    Issued Jul 2020
+
+    Credential ID V492QQ4JJKEB
+
+    See credential
+
+-   End-to-End Machine Learning with TensorFlow on GCP
+
+    Google
+
+    Issued Jul 2020
+
+    Credential ID QLDMNADDBSRR
+
+    See credential
+
+-   Image Understanding with TensorFlow on GCP
+
+    Google
+
+    Issued Jul 2020
+
+    Credential ID HY4HSSY8JSPN
+
+    See credential
+
+-   Production Machine Learning Systems
+
+    Google
+
+    Issued Jul 2020
+
+    Credential ID THZZNW22LHKT
+
+    See credential
+
+-   Recommendation Systems with TensorFlow on GCP
+
+    Google
+
+    Issued Jul 2020
+
+    Credential ID 2D4LT28697TC
+
+    See credential
+
+-   Sequence Models for Time Series and Natural Language Processing
+
+    Google
+
+    Issued Jul 2020
+
+    Credential ID 6XUV7YJFM3ZA
+
+    See credential
+
+-   Building Batch Data Pipelines on GCP
+
+    Google
+
+    Issued May 2020
+
+    Credential ID 5QYSK9E5EAFN
+
+    See credential
+
+-   Building Resilient Streaming Analytics Systems on GCP
+
+    Google
+
+    Issued May 2020
+
+    Credential ID FYQW7D4F6PD4
+
+    See credential
+
+-   Data Engineering with Google Cloud Specialization
+
+    Google
+
+    Issued May 2020
+
+    Credential ID EPZ3WQFC423E
+
+    See credential
+
+-   Modernizing Data Lakes and Data Warehouses with GCP
+
+    Google
+
+    Issued May 2020
+
+    Credential ID 393P3HLZWY8H
+
+    See credential
+
+-   Smart Analytics, Machine Learning, and AI on GCP
+
+    Google
+
+    Issued May 2020
+
+    Credential ID AK77VUVN4ARJ
+
+    See credential
+
+-   Google Cloud Platform Big Data and Machine Learning Fundamentals
+
+    Google
+
+    Issued Apr 2020
+
+    Credential ID 2Q35NYHYMW5E
+
+    See credential
+
+-   Devenez Mentor Evaluateur
+
+    OpenClassrooms
+
+    Issued Feb 2019
+
+    Credential ID 8151214336
+
+    See credential
+
+-   Advanced AI: Deep Reinforcement Learning in Python
+
+    Udemy
+
+    Issued Aug 2018
+
+    Credential ID UC-5FM0CC9S
+
+    See credential
+
+-   Artificial Intelligence: Reinforcement Learning in Python
+
+    Udemy
+
+    Issued Jul 2018
+
+    Credential ID UC-XALJEH7G
+
+    See credential
+
+-   Concevez un site avec Flask
+
+    OpenClassrooms
+
+    Issued Jul 2018
+
+    Credential ID 5343531703
+
+    See credential
+
+-   Les étapes de la vie du Mentor
+
+    OpenClassrooms
+
+    Issued Jul 2018
+
+    Credential ID 8431716200
+
+    See credential
+
+-   Devenez Mentor chez OpenClassrooms
+
+    OpenClassrooms
+
+    Issued May 2018
+
+    Credential ID 6193593386
+
+    See credential
+
+-   Complete Guide to ElasticSearch
+
+    Udemy
+
+    Issued Mar 2018
+
+    Credential ID UC-H5AJQVA3
+
+    See credential
+
+-   Introduction to Hadoop
+
+    The Linux Foundation
+
+    Issued Oct 2017
+
+    Credential ID ad676a8fe7994edea33516b80b540971
+
+    See credential
+
+-   Artificial Intelligence Nanodegree
+
+    Udacity
+
+    Issued Sep 2017
+
+    Credential ID PV7A7EAA
+
+    See credential
+
+-   High Performance Computing
+
+    University of Luxembourg
+
+    Issued Feb 2017
+
+    See credential
+
+-   Machine Learning
+
+    Standford University
+
+    Issued Sep 2015
+
+    Credential ID Grade: 97%
+
+    See credential
+
+-   TOEIC
+
+    Listening, Reading
+
+    Issued Jan 2014
+
+    Credential ID Score: 975/990
+
+Publications
+
+-   Is AI/ML Monitoring just Data Engineering? 🤔
+
+    MLOps Community   July 24, 2023  
+
+    While the future of machine learning and MLOps is being debated,
+    practitioners still need to attend to their machine learning models
+    in production. This is no easy task, as ML engineers must constantly
+    assess the quality of the data that enters and exits their
+    pipelines, and ensure that their models generate the correct
+    predictions. To assist ML engineers with this challenge, several
+    AI/ML monitoring solutions have been developed.
+    In this article, I will discuss the nature of AI/ML… Show more
+
+    While the future of machine learning and MLOps is being debated,
+    practitioners still need to attend to their machine learning models
+    in production. This is no easy task, as ML engineers must constantly
+    assess the quality of the data that enters and exits their
+    pipelines, and ensure that their models generate the correct
+    predictions. To assist ML engineers with this challenge, several
+    AI/ML monitoring solutions have been developed.
+    In this article, I will discuss the nature of AI/ML monitoring and
+    how it relates to data engineering. First, I will present the
+    similarities between AI/ML monitoring and data engineering. Second,
+    I will enumerate additional features that AI/ML monitoring solutions
+    can provide. Third, I will briefly touch on the topic of AI/ML
+    observability and its relation to AI/ML monitoring. Finally, I will
+    provide my conclusion about the field of AI/ML monitoring and how it
+    should be considered to ensure the success of your AI/ML project.
+    Show less
+
+    See publication
+
+-   A great MLOps project should start with a good Python Package 🐍
+
+    MLOps Community   June 28, 2023  
+
+    In this article, I present the implementation of a Python package on
+    GitHub designed to support MLOps initiatives. The goal of this
+    package is to make the coding workflow of data scientists and ML
+    engineers as flexible, robust, and productive as possible. First, I
+    start by motivating the use of Python packages. Then, I provide some
+    tools and tips you can include in your MLOps project. Finally, I
+    explain the follow-up steps required to take this package to the
+    next level and make it work in your… Show more
+
+    In this article, I present the implementation of a Python package on
+    GitHub designed to support MLOps initiatives. The goal of this
+    package is to make the coding workflow of data scientists and ML
+    engineers as flexible, robust, and productive as possible. First, I
+    start by motivating the use of Python packages. Then, I provide some
+    tools and tips you can include in your MLOps project. Finally, I
+    explain the follow-up steps required to take this package to the
+    next level and make it work in your environment. Show less
+
+    See publication
+
+-   Fixing the MLOps Survey on LLMs with ChatGPT API: Lessons Learned
+
+    MLOps Community   May 11, 2023  
+
+    Large Language Model (LLM) is such an existing topic. Since the
+    release of ChatGPT, we saw a surge of innovation ranging from
+    education mentorship to finance advisory. Each week is a new
+    opportunity for addressing new kinds of problems, increasing human
+    productivity, or improving existing solutions. Yet, we may wonder if
+    this is just a new hype cycle or if organizations are truly adopting
+    LLMs at scale …
+    On March 2023, the MLOps Community issued a survey about LLMs in
+    production to… Show more
+
+    Large Language Model (LLM) is such an existing topic. Since the
+    release of ChatGPT, we saw a surge of innovation ranging from
+    education mentorship to finance advisory. Each week is a new
+    opportunity for addressing new kinds of problems, increasing human
+    productivity, or improving existing solutions. Yet, we may wonder if
+    this is just a new hype cycle or if organizations are truly adopting
+    LLMs at scale …
+    On March 2023, the MLOps Community issued a survey about LLMs in
+    production to picture the state of adoption. The survey is full of
+    interesting insights, but there is a catch: 80% of the questions are
+    open-ended, which means respondents answered the survey freely from
+    a few keywords to full sentences. I volunteered to clean up the
+    answers with the help of ChatGPT and let the community get a grasp
+    of the survey experiences.
+    In this article, I present the steps and lessons learned from my
+    journey to shed some light on the MLOps survey on LLMs. I’m first
+    going to present the goal and questions of the survey. Then, I will
+    explain how I used ChatGPT to review the data and standardize the
+    content. Finally, I’m going to evaluate the performance of ChatGPT
+    compared to a manual review. Show less
+
+    See publication
+
+-   Kubeflow: The Machine Learning Toolkit for Kubernetes
+
+    MLOps Community in Luxembourg   April 26, 2023  
+
+    See publication
+
+-   MLflow: An open source platform for the machine learning lifecycle
+
+    MLOps Community in Luxembourg   April 26, 2023  
+
+    See publication
+
+-   We need POSIX for MLOps
+
+    MLOps Community   April 21, 2023  
+
+    If you work on MLOps, you must navigate an ever-growing landscape of
+    tools and solutions. This is both an intense source of stimulation
+    and fatigue for MLOps practitioners.
+    Vendors and users face the same problem: How can we combine all
+    these tools without the combinatorial complexity of creating custom
+    integrations?
+    In this article, I propose a solution analogous to POSIX to address
+    this challenge. First, I motivate the creation of common protocols
+    and schemas for combining MLOps… Show more
+
+    If you work on MLOps, you must navigate an ever-growing landscape of
+    tools and solutions. This is both an intense source of stimulation
+    and fatigue for MLOps practitioners.
+    Vendors and users face the same problem: How can we combine all
+    these tools without the combinatorial complexity of creating custom
+    integrations?
+    In this article, I propose a solution analogous to POSIX to address
+    this challenge. First, I motivate the creation of common protocols
+    and schemas for combining MLOps tools. Second, I present a
+    high-level architecture to support implementation. Third, I conclude
+    with the benefits and limitations of standardizing MLOps. Show less
+
+    See publication
+
+-   How to install Kubeflow Pipelines v2 on Apple Silicon
+
+    Medium   September 24, 2022  
+
+    Kubeflow Pipelines (KFP) is a powerful platform for building machine
+    learning pipelines at scale with Kubernetes. The platform is well
+    supported on major cloud platforms such as GCP (Vertex AI Pipelines)
+    or AWS (Kubeflow on AWS). However, installing KFP on Apple Silicon
+    (macOS 12.5.1 with Apple M1 Pro) proved to be more challenging than
+    I imagined. Thus, I wanted to share my experience and tips to
+    install KFP as easily as possible on your shiny Mac.
+    In this article, I present 4 steps to… Show more
+
+    Kubeflow Pipelines (KFP) is a powerful platform for building machine
+    learning pipelines at scale with Kubernetes. The platform is well
+    supported on major cloud platforms such as GCP (Vertex AI Pipelines)
+    or AWS (Kubeflow on AWS). However, installing KFP on Apple Silicon
+    (macOS 12.5.1 with Apple M1 Pro) proved to be more challenging than
+    I imagined. Thus, I wanted to share my experience and tips to
+    install KFP as easily as possible on your shiny Mac.
+    In this article, I present 4 steps to install Kubeflow on Apple
+    Silicon, using Rancher Desktop for setting up Docker/Kubernetes. In
+    the end, I list the problems I encountered during the installation
+    of Kubeflow Pipelines. Show less
+
+    See publication
+
+-   The Programming Trade-Off: Purpose, Productivity, Performance
+
+    Medium   August 15, 2019  
+
+    As programmers, we are continuously looking for languages that are
+    performant, productive, and general purpose. Is there any
+    programming language that currently satisfies these properties? Can
+    we ever create one?
+    In this article, I present a fundamental trade-off that affects the
+    design of programming languages and the success of software
+    projects.
+
+    See publication
+
+-   Creating better ground truth to further understand Android malware: A large scale mining approach based on antivirus labels and malicious artifacts
+
+    University of Luxembourg   July 1, 2019  
+
+    Mobile applications are essential for interacting with technology
+    and other people. With more than 2 billion devices deployed all over
+    the world, Android offers a thriving ecosystem by making accessible
+    the work of thousands of developers on digital marketplaces such as
+    Google Play. Nevertheless, the success of Android also exposes
+    millions of users to malware authors who seek to siphon private
+    information and hijack mobile devices for their benefits.
+    To fight against the proliferation… Show more
+
+    Mobile applications are essential for interacting with technology
+    and other people. With more than 2 billion devices deployed all over
+    the world, Android offers a thriving ecosystem by making accessible
+    the work of thousands of developers on digital marketplaces such as
+    Google Play. Nevertheless, the success of Android also exposes
+    millions of users to malware authors who seek to siphon private
+    information and hijack mobile devices for their benefits.
+    To fight against the proliferation of Android malware, the security
+    community embraced machine learning, a branch of artificial
+    intelligence that powers a new generation of detection systems.
+    Machine learning algorithms, however, require a substantial number
+    of qualified samples to learn the classification rules enforced by
+    security experts. Unfortunately, malware ground truths are
+    notoriously hard to construct due to the inherent complexity of
+    Android applications and the global lack of public information about
+    malware. In a context where both information and human resources are
+    limited, the security community is in demand for new approaches to
+    aid practitioners to accurately define Android malware, automate
+    classification decisions, and improve the comprehension of Android
+    malware.
+    This dissertation proposes three solutions to assist with the
+    creation of malware ground truths. Show less
+
+    See publication
+
+-   Euphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
+
+    MSR 2017   May 21, 2017  
+
+    Android malware is now pervasive and evolving rapidly. Thousands of
+    malware samples are discovered every day with new models of attacks.
+    The growth of these threats has come hand in hand with the
+    proliferation of collective repositories sharing the latest
+    specimens. Having access to a large number of samples opens new
+    research directions aiming at efficiently vetting apps. However,
+    automatically inferring a reference ground-truth from those
+    repositories is not straightforward and can… Show more
+
+    Android malware is now pervasive and evolving rapidly. Thousands of
+    malware samples are discovered every day with new models of attacks.
+    The growth of these threats has come hand in hand with the
+    proliferation of collective repositories sharing the latest
+    specimens. Having access to a large number of samples opens new
+    research directions aiming at efficiently vetting apps. However,
+    automatically inferring a reference ground-truth from those
+    repositories is not straightforward and can inadvertently lead to
+    unforeseen misconceptions. On the one hand, samples are often
+    mislabeled as different parties use distinct naming schemes for the
+    same sample. On the other hand, samples are frequently misclassified
+    due to conceptual errors made during labeling processes.
+    In this paper, we analyze the associations between all labels given
+    by different vendors and we propose a system called EUPHONY to
+    systematically unify common samples into family groups. The key
+    novelty of our approach is that no prior knowledge of malware
+    families is needed. We evaluate our approach using reference
+    datasets and more than 0.4 million additional samples outside of
+    these datasets. Results show that EUPHONY provides competitive
+    performance against the state-of-the-art. Show less
+
+    See publication
+
+-   On the Lack of Consensus in Anti-Virus Decisions: Metrics and Insights on Building Ground Truths of Android Malware
+
+    DIMVA 2016   July 7, 2016  
+
+    There is generally a lack of consensus in Antivirus (AV) engines'
+    decisions on a given sample. This challenges the building of
+    authoritative ground-truth datasets. Instead, researchers and
+    practitioners may rely on unvalidated approaches to build their
+    ground truth, e.g., by considering decisions from a selected set of
+    Antivirus vendors or by setting up a threshold number of positive
+    detections before classifying a sample. Both approaches are biased
+    as they implicitly either decide on ranking… Show more
+
+    There is generally a lack of consensus in Antivirus (AV) engines'
+    decisions on a given sample. This challenges the building of
+    authoritative ground-truth datasets. Instead, researchers and
+    practitioners may rely on unvalidated approaches to build their
+    ground truth, e.g., by considering decisions from a selected set of
+    Antivirus vendors or by setting up a threshold number of positive
+    detections before classifying a sample. Both approaches are biased
+    as they implicitly either decide on ranking AV products, or they
+    consider that all AV decisions have equal weights. In this paper, we
+    extensively investigate the lack of agreement among AV engines.
+    To that end, we propose a set of metrics that quantitatively
+    describe the different dimensions of this lack of consensus. We show
+    how our metrics can bring important insights by using the detection
+    results of 66 AV products on 2 million Android apps as a case study.
+    Our analysis focuses not only on AV binary decision but also on the
+    notoriously hard problem of labels that AVs associate with
+    suspicious files, and allows to highlight biases hidden in the
+    collection of a malware ground truth---a foundation stone of any
+    machine learning-based malware detection approach. Show less
+
+    See publication
+
+Courses
+
+-   Artificial Intelligence
+
+    https://www.edx.org
+
+-   Data Science at Scale
+
+    https://www.coursera.org/
+
+-   Descriptive Statistics
+
+    https://www.edx.org/
+
+-   Developing Android Apps
+
+    https://eu.udacity.com/
+
+-   Full Stack Foundations
+
+    https://www.udacity.com/
+
+-   Functional Programming
+
+    https://www.edx.org/
+
+-   Introduction to Logic
+
+    https://www.coursera.org/
+
+-   Knowledge-Based AI: Cognitive Systems
+
+    https://eu.udacity.com/
+
+-   Machine Learning
+
+    https://www.coursera.org/
+
+-   Mathematics for Computer Science
+
+    https://ocw.mit.edu/
+
+-   Nanodegree Program: Artificial Intelligence
+
+    https://eu.udacity.com/
+
+-   Paradigms of Computer Programming
+
+    https://www.edx.org
+
+-   Statistics Inference
+
+    https://www.edx.org/
+
+-   Statistics Probability
+
+    https://www.edx.org/
+
+-   ChatGPT Prompt Engineering for Developers
+
+    Deeplearning.ai
+
+-   Introduction to Data-Centric AI
+
+    -
+
+-   LangChain for LLM Application Development - DeepLearning.ai
+
+    -
+
+-   LangChain: Chat with Your Data - DeepLearning.ai
+
+    -
+
+-   Hugging Face Course
+
+    huggingface.co/course/
+
+Projects
+
+-   MLOps Python Package
+
+     Jun 2023 - Jun 2023 
+
+    Kickstart your MLOps initiative with a flexible, robust, and
+    productive Python package.
+    https://github.com/fmind/mlops-python-package
+
+-   Fixing the MLOps Survey with ChatGPT
+
+     May 2023 - May 2023 
+
+    Fixing the MLOps Survey on LLMs with ChatGPT API.
+    https://fmind.medium.com/fixing-the-mlops-survey-on-llms-with-chatgpt-api-lessons-learned-62d90e721331
+
+    See project
+
+-   Kubeflow Demo
+
+     Apr 2023 - Apr 2023 
+
+    Kubeflow demo for the MLOps Community Meetup in Luxembourg.
+
+    See project
+
+-   MLflow Demo
+
+     Apr 2023 - Apr 2023 
+
+    MLflow demo for the MLOps Community Meetup in Luxembourg.
+
+    See project
+
+-   onet
+
+     Aug 2020 - Sep 2020 
+
+    Train and predict procedures of DNN for binary image classification
+
+    See project
+
+-   fincrawl
+
+     Nov 2019 - Dec 2019 
+
+    Crawl documents, metadata, and files from financial institutions
+
+    See project
+
+-   invest
+
+     Aug 2019 - Sep 2019 
+
+    Stock market analysis focused on dividends
+
+    See project
+
+-   parsoc
+
+     Jul 2019 - Sep 2019 
+
+    Convert docx files to json
+
+    See project
+
+-   Bigdata Tutorials
+
+     Sep 2015 - Jul 2019 
+
+    Tutorials for the Big Data course @ uni.lu
+
+    See project
+
+-   STASE: A set of statistical metrics to better understand and qualify malware datasets
+
+     Apr 2016 - Jul 2019 
+
+    A handful of statistical metrics to better understand and qualify
+    malware datasets
+
+    See project
+
+-   apkworkers
+
+     Sep 2015 - Jul 2019 
+
+    A celery application to distribute Android malware analysis
+
+    See project
+
+-   servalx
+
+     Sep 2015 - Jul 2019 
+
+    A set of tools and modules to process Android malware with Androzoo
+
+    See project
+
+-   Euphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
+
+     Mar 2017 - Mar 2019 
+
+    Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for
+    Android Malware
+
+    See project
+
+-   Automatic Speech Recognition with Tensorflow
+
+     Sep 2017 - Sep 2017 
+
+    An automatic speech-recognition system based on Tensorflow
+
+    See project
+
+-   Dog Recognition with Tensorflow
+
+     Aug 2017 - Aug 2017 
+
+    A machine-learning model train to recognize dogs, even from human
+    faces
+
+    See project
+
+-   genius
+
+     Jun 2017 - Jul 2017 
+
+    An implementation of LISP Scheme based on Haskell
+
+    See project
+
+-   Alexa History Skill
+
+     Jun 2017 - Jun 2017 
+
+    A Alexa skill that provides year-dated facts on demand
+
+    See project
+
+-   Air Cargo Planning System
+
+     Feb 2017 - Apr 2017 
+
+    An automated Air Cargo transport system based on AI planning
+
+    See project
+
+-   Sign Language Recognition System
+
+     Feb 2017 - Apr 2017 
+
+    A sign recognition system based on Hidden Markov Model
+
+    See project
+
+-   AI Agent for the Isolation Game
+
+     Mar 2017 - Mar 2017 
+
+    An AI game agent to play the Isolation game
+
+    See project
+
+-   Sudoku Solver
+
+     Jan 2017 - Feb 2017 
+
+    A Diagonal Sudoku solver implemented with Python
+
+    See project
+
+-   lkml
+
+     Nov 2016 - Jan 2017 
+
+    Gather emails from https://lkml.org/
+
+    See project
+
+-   Master 2 School Projects
+
+     Sep 2013 - Jun 2014 
+
+    School projects from 2013 to 2014 - Master 2 Sécurité des Systèmes
+    d'Information (Metz)
+
+    See project
+
+-   chattail
+
+     Dec 2013 - Mar 2014 
+
+    Send log streams over XMPP to monitor your systems
+
+    See project
+
+-   Master 1 School Projects
+
+     Jun 2012 - Sep 2013 
+
+    See project
+
+-   Bachelor School Projects
+
+     Jun 2011 - Sep 2012 
+
+    See project
+
+-   Professional Bachelor School Project
+
+     Sep 2009 - Jun 2010 
+
+    See project
+
+-   https://github.com/fmind/mlops-python-package
+
+    -
+
+    Kickstart your MLOps initiative with a flexible, robust, and
+    productive Python package.
+
+Languages
+
+-   Français
+
+    Native or bilingual proficiency
+
+-   Anglais
+
+    Full professional proficiency
+
+View Médéric’s full profile
+
+-   
+
+    See who you know in common
+
+-   
+
+    Get introduced
+
+-   
+
+    Contact Médéric directly
+
+Sign in to view full profile
+
+Sign in to view Médéric’s full profile
+
+Sign in
+
+Welcome back
+
+Email or phone
+
+Password
+
+Show
+
+Forgot password?
+
+Sign in
+
+or
+
+By clicking Continue, you agree to LinkedIn’s User Agreement, Privacy
+Policy, and Cookie Policy.
+
+New to LinkedIn? Join now
+
+or
+
+By clicking Continue, you agree to LinkedIn’s User Agreement, Privacy
+Policy, and Cookie Policy.
+
+New to LinkedIn? Join now
+
+Explore collaborative articles
+
+We’re unlocking community knowledge in a new way. Experts add insights
+directly into each article, started with the help of AI.
+
+Explore More
+
+Médéric’s public profile badge
+
+Include this LinkedIn profile on other websites
+
+Médéric HURIER
+
+Freelancer: AI/ML/MLOps Engineer | Data Scientist | MLOps Community
+Organizer | OpenClassrooms Mentor | Hacker | PhD
+
+-   Lead MLOps Engineer at Decathlon Technology
+
+-   University of Luxembourg
+
+View profile
+
+View profile badges
+
+-   LinkedIn © 2023
+
+-   About
+
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+
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+
+-   Guest Controls
+
+-   Community Guidelines
+
+-   -   العربية (Arabic)
+    -   Čeština (Czech)
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+    -   Deutsch (German)
+    -   English (English)
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+    -   Français (French)
+    -   हिंदी (Hindi)
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+    -   한국어 (Korean)
+    -   Bahasa Malaysia (Malay)
+    -   Nederlands (Dutch)
+    -   Norsk (Norwegian)
+    -   Polski (Polish)
+    -   Português (Portuguese)
+    -   Română (Romanian)
+    -   Русский (Russian)
+    -   Svenska (Swedish)
+    -   ภาษาไทย (Thai)
+    -   Tagalog (Tagalog)
+    -   Türkçe (Turkish)
+    -   Українська (Ukrainian)
+    -   简体中文 (Chinese (Simplified))
+    -   正體中文 (Chinese (Traditional))
+
+    Language
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+Sign in to view Médéric’s full profile
+
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+
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+
+[]

invoke.yaml

@@ -0,0 +1,23 @@
+# https://docs.pyinvoke.org/en/latest/index.html
+
+run:
+  echo: true
+app:
+  path: "app.py"
+database:
+  path: "database"
+linkedin:
+  html: "files/linkedin.html"
+  markdown: "files/linkedin.md"
+  text: "files/linkedin.txt"
+mypy:
+  cache: ".mypy_cache/"
+pip:
+  requirements: "requirements.txt"
+  requirements_dev: "requirements-dev.txt"
+python:
+  files: "tasks/ *.py"
+  path: "python3"
+venv:
+  path: ".venv/"
+  python: ".venv/bin/python"

lib.py

@@ -0,0 +1,45 @@
+"""Library of the project."""
+# pylint: disable=wrong-import-position
+
+# %% IMPORTS
+
+__import__("pysqlite3")
+
+import os
+import sys
+
+# https://docs.trychroma.com/troubleshooting#sqlite
+sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
+
+import chromadb
+from chromadb.utils import embedding_functions
+
+# %% TYPINGS
+
+Collection = chromadb.Collection
+
+# %% CONFIGS
+
+DATABASE_COLLECTION = "resume"
+OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+
+# %% FUNCTIONS
+
+
+def get_database_client(path: str) -> chromadb.API:
+    """Get a persistent client to the Chroma DB."""
+    settings = chromadb.Settings(
+        allow_reset=True,
+        anonymized_telemetry=False,
+    )
+    return chromadb.PersistentClient(path=path, settings=settings)
+
+
+def get_embedding_function(
+    model_name: str = "text-embedding-ada-002", api_key: str = OPENAI_API_KEY
+) -> embedding_functions.EmbeddingFunction:
+    """Get the embedding function for Chroma DB collections."""
+    return embedding_functions.OpenAIEmbeddingFunction(
+        model_name=model_name,
+        api_key=api_key,
+    )

packages.txt

@@ -0,0 +1 @@
+# https://huggingface.co/docs/hub/spaces-dependencies

pyproject.toml

@@ -0,0 +1,17 @@
+# https://pip.pypa.io/en/stable/reference/build-system/pyproject-toml/
+
+# https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html
+[tool.black]
+
+# https://pycqa.github.io/isort/docs/configuration/options.html
+[tool.isort]
+profile = "black"
+
+# https://mypy.readthedocs.io/en/stable/config_file.html
+[tool.mypy]
+plugins = ["pydantic.mypy"]
+ignore_missing_imports = true
+
+# https://pylint.pycqa.org/en/latest/user_guide/configuration/index.html
+[tool.pylint]
+disable = []

requirements-dev.txt

@@ -0,0 +1,8 @@
+# https://pip.pypa.io/en/stable/reference/requirements-file-format/
+
+black
+invoke
+ipykernel
+isort
+mypy
+pylint

requirements.txt

@@ -0,0 +1,8 @@
+# https://pip.pypa.io/en/stable/reference/requirements-file-format/
+
+chromadb==0.4.13
+gradio==3.47.1
+openai==0.28.1
+pysqlite3-binary==0.5.1
+tiktoken==0.5.1
+

resume.code-workspace

@@ -0,0 +1,29 @@
+//  https://code.visualstudio.com/docs/getstarted/settings
+{
+	"folders": [
+		{
+			"path": "."
+		}
+	],
+	"settings": {
+		"editor.formatOnSave": true,
+		"python.defaultInterpreterPath": ".venv/bin/python3",
+		"[python]": {
+			"editor.codeActionsOnSave": {
+				"source.organizeImports": true
+			},
+			"editor.defaultFormatter": "ms-python.black-formatter"
+		},
+	},
+	"extensions": {
+		"recommendations": [
+			"dchanco.vsc-invoke",
+			"ms-python.black-formatter",
+			"ms-python.isort",
+			"ms-python.pylint",
+			"ms-python.python",
+			"ms-python.vscode-pylance",
+			"ms-python.mypy-type-checker",
+		]
+	}
+}

tasks/__init__.py

@@ -0,0 +1,22 @@
+"""Task collections for the project."""
+# pylint: disable=redefined-builtin
+# mypy: ignore-errors
+
+# %% IMPORTS
+
+from invoke import Collection
+
+from . import check, clean, convert, format, install, run
+
+# %% NAMESPACES
+
+ns = Collection()
+
+# %% COLLECTIONS
+
+ns.add_collection(run, default=True)
+ns.add_collection(check)
+ns.add_collection(clean)
+ns.add_collection(convert)
+ns.add_collection(format)
+ns.add_collection(install)

tasks/check.py

@@ -0,0 +1,33 @@
+"""Check tasks for the project."""
+# pylint: disable=redefined-builtin
+
+# %% IMPORTS
+
+from invoke import task
+from invoke.context import Context
+
+# %% TASKS
+
+
+@task
+def type(ctx: Context) -> None:
+    """Check the types with mypy."""
+    ctx.run(f"{ctx.venv.python} -m mypy {ctx.python.files}")
+
+
+@task
+def code(ctx: Context) -> None:
+    """Check the codes with pylint."""
+    ctx.run(f"{ctx.venv.python} -m pylint {ctx.python.files}")
+
+
+@task
+def format(ctx: Context) -> None:
+    """Check the formats with isort and black."""
+    ctx.run(f"{ctx.venv.python} -m isort --check {ctx.python.files}")
+    ctx.run(f"{ctx.venv.python} -m black --check --quiet {ctx.python.files}")
+
+
+@task(pre=[type, code, format], default=True)
+def all(_: Context) -> None:
+    """Run all check tasks."""

tasks/clean.py

@@ -0,0 +1,38 @@
+"""Clean tasks for the project."""
+# pylint: disable=redefined-builtin
+
+# %% IMPORTS
+
+from invoke import task
+from invoke.context import Context
+
+# %% TASKS
+
+
+@task
+def install(ctx: Context) -> None:
+    """Clean the install."""
+    ctx.run(f"rm -rf {ctx.venv.path}")
+
+
+@task
+def mypy(ctx: Context) -> None:
+    """Clean the mypy cache."""
+    ctx.run(f"rm -rf {ctx.mypy.cache}")
+
+
+@task
+def python(ctx: Context) -> None:
+    """Clean python files and folders."""
+    ctx.run("find . -type f -name '*.py[co]' -delete")
+    ctx.run("find . -type d -name __pycache__ -delete")
+
+
+@task(pre=[mypy, python], default=True)
+def all(_: Context) -> None:
+    """Run all clean tasks."""
+
+
+@task(pre=[all, install])
+def reset(_: Context) -> None:
+    """Reset the project state."""

tasks/convert.py

@@ -0,0 +1,36 @@
+"""Convert files for the project."""
+# pylint: disable=redefined-builtin
+
+# %% IMPORTS
+
+from invoke import task
+from invoke.context import Context
+
+# %% TASKS
+
+
+@task
+def linkedin(ctx: Context) -> None:
+    """Convert the LinkedIn files."""
+    ctx.run(
+        f"""pandoc --to=plain --from=html \
+            --output={ctx.linkedin.text} \
+            {ctx.linkedin.html}
+        """
+    )
+
+
+@task
+def database(ctx: Context) -> None:
+    """Import files to the database."""
+    ctx.run(
+        f"""{ctx.venv.python} database.py \
+            --database={ctx.database.path} \
+            {ctx.linkedin.markdown}
+        """
+    )
+
+
+@task(pre=[linkedin], default=True)
+def all(_: Context) -> None:
+    """Run all convert tasks."""

tasks/format.py

@@ -0,0 +1,26 @@
+"""Format tasks for the project."""
+# pylint: disable=redefined-builtin
+
+# %% IMPORTS
+
+from invoke import task
+from invoke.context import Context
+
+# %% TASKS
+
+
+@task
+def imports(ctx: Context) -> None:
+    """Format code imports with isort."""
+    ctx.run(f"{ctx.venv.python} -m isort {ctx.python.files}")
+
+
+@task
+def sources(ctx: Context) -> None:
+    """Format code sources with black."""
+    ctx.run(f"{ctx.venv.python} -m black {ctx.python.files}")
+
+
+@task(pre=[imports, sources], default=True)
+def all(_: Context) -> None:
+    """Run all format tasks."""

tasks/install.py

@@ -0,0 +1,32 @@
+"""Install tasks for the project."""
+# pylint: disable=redefined-builtin
+
+# %% IMPORTS
+
+from invoke import task
+from invoke.context import Context
+
+# %% TASKS
+
+
+@task
+def venv(ctx: Context) -> None:
+    """Create a virtual environment."""
+    ctx.run(f"{ctx.python.path} -m venv {ctx.venv.path}")
+
+
+@task
+def main(ctx: Context) -> None:
+    """Install the main dependencies."""
+    ctx.run(f"{ctx.venv.python} -m pip install -r {ctx.pip.requirements}")
+
+
+@task
+def dev(ctx: Context) -> None:
+    """Install the development dependencies."""
+    ctx.run(f"{ctx.venv.python} -m pip install -r {ctx.pip.requirements_dev}")
+
+
+@task(pre=[venv, main, dev], default=True)
+def all(_: Context) -> None:
+    """Run all install tasks."""

tasks/run.py

@@ -0,0 +1,15 @@
+"""Run tasks for the project."""
+# pylint: disable=redefined-builtin
+
+# %% IMPORTS
+
+from invoke import task
+from invoke.context import Context
+
+# %% TASKS
+
+
+@task(default=True)
+def app(ctx: Context) -> None:
+    """Run the main application."""
+    ctx.run(f"gradio {ctx.app.path}")