🐔Production guide

Level up your skills in a production setting.

The ZenML production guide builds upon the Starter guide and is the next step in the MLOps Engineer journey with ZenML. If you're an ML practitioner hoping to implement a proof of concept within your workplace to showcase the importance of MLOps, this is the place for you.

This guide will focus on shifting gears from running pipelines locally on your machine, to running them in production in the cloud. We'll cover:

Deploying ZenML

Understanding stacks

Connecting remote storage

Orchestrating on the cloud

Configuring the pipeline to scale compute

Configure a code repository

Like in the starter guide, make sure you have a Python environment ready and virtualenv installed to follow along with ease. As now we are dealing with cloud infrastructure, you'll also want to select one of the major cloud providers (AWS, GCP, Azure), and make sure the respective CLIs are installed and authorized.

By the end, you will have completed an end-to-end MLOps project that you can use as inspiration for your own work. Let's get right into it!

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Last updated 15 days ago