Jan-Feb Learning 2019

What’s This?

I’m trying to give myself at least half an hour during the workdays (or at least blocking two hours or so a week at least) to learn something new – namely taking classes/reviewing what I know on Treehouse, reading job related articles, and reading career-related books. Tracking notables here on a monthly basis as a self-commitment and to retain in memory and as reference. I tell off posting this the last six months with work and life has been insanely busy and my notes inconsistent across proprietary work versus my own, but worth a round-up here. Posting with good intentions for next year. Reminding myself that if I don’t capture every bit I did, it’s alright. Just keep yourself accountable.

Books Read

Inspired: How To Create Products Customers Love

Some favorite quotes from Kindle highlights:

  • This means constantly creating new value for their customers and for their business. Not just tweaking and optimizing existing products (referred to as value capture) but, rather, developing each product to reach its full potential. Yet, many large, enterprise companies have already embarked on a slow death spiral. They become all about leveraging the value and the brand that was created many years or even decades earlier. The death of an enterprise company rarely happens overnight, and a large company can stay afloat for many years. But, make no mistake about it, the organization is sinking, and the end state is all but certain.
  • The little secret in product is that engineers are typically the best single source of innovation; yet, they are not even invited to the party in this process.
  • To summarize, these are the four critical contributions you need to bring to your team: deep knowledge (1) of your customer, (2) of the data, (3) of your business and its stakeholders, and (4) of your market and industry.
  • In the products that succeed, there is always someone like Jane, behind the scenes, working to get over each and every one of the objections, whether they’re technical, business, or anything else. Jane led the product discovery work and wrote the first spec for AdWords. Then she worked side by side with the engineers to build and launch the product, which was hugely successful.
  • Four key competencies: (1) team development, (2) product vision, (3) execution, and (4) product culture.
  • It’s usually easy to see when a company has not paid attention to the architecture when they assemble their teams—it shows up a few different ways. First, the teams feel like they are constantly fighting the architecture. Second, interdependencies between teams seem disproportionate. Third, and really because of the first two, things move slowly, and teams don’t feel very empowered.
  • I strongly prefer to provide the product team with a set of business objectives—with measurable goals—and then the team makes the calls as to what are the best ways to achieve those goals. It’s part of the larger trend in product to focus on outcome and not output.

In my experience working with companies, only a few companies are strong at both innovation and execution. Many are good at execution but weak at innovation; some are strong at innovation and just okay at execution; and a depressing number of companies are poor at both innovation and execution (usually older companies that lost their product mojo a long time ago, but still have a strong brand and customer base to lean on).


Articles Read

Machine learning – is the emperor wearing clothes

  1. “The purpose of a machine learning algorithm is to pick the most sensible place to put a fence in your data.”
  2. Different algorithms, eg. vector classifier, decision tree, neural network, use different kinds of fences
  3. Neural networks give you a very flexible boundary which is why they’re so hot now

Some Key Machine Learning Definitions

  1. “A model is overfitting if it fits the training data too well and there is a poor generalization of new data.”
  2. Regularization is used to estimate a preferred complexity of a machine learning model so that the model generalizes to avoid overfitting and underfitting by adding a penalty on different parameters of the model – but this reduces the freedom of the model
  3. “Hyperparameters cannot be estimated from the training data. Hyperparameters of a model are set and tuned depending on a combination of some heuristics and the experience and domain knowledge of the data scientist.”

Audiences-Based Planning versus Index-Based Planning

  • Index is the relative composition of a target audience of a specific program or network as compared to the average size audience in TV universe to give marketers/agencies a gauge of the value of a program or network relative to others using the relative concentrations of a specific target audience
  • Audience-based buying is not account for relative composition of an audience or the context within the audience is likely to be found but rather values the raw number of individuals in a target audience who watch given program, their likelihood of being exposed to an ad, and the cost of reaching them with a particular spot. Really it’s buying audiences versus buying a particular program
  • Index-based campaigns follow TV planning model: maximum number of impressions of a given audience at minimum price -> buy high-indexing media against a traditional age/demo: note this doesn’t include precision index targeting
  • Huge issue is tv is insanely fragmented so even if campaigns are hitting GRP targets, they’re doing so by increasing frequency rather than total reach
  • Note: GRP is measure of a size of an ad campaign by medium or schedule – not size of audience reached. GRPs quantify impressions as a percentage of target population and this percent may thus be greater than 100. This is meant to measure impressions in relation to number of people and is the metric used to compare strength of components in a media plan. There are several ways to calculate GRPs, eg. GRP % = 100 * Reach % * Avg Freq or even just rating TV rating with a rating of 4 gets placed on 5 episodes = 20 GRPS
  • Index-based planning is about impressions delivered over balancing of reach and frequency. Audience-based is about reaching likely customers for results
  • DSPs, etc. should be about used optimized algorithms to assign users probability of being exposed to a spot to maximize probabilities of a specific target-audience reach
  • Audience-based planning is about maximizing reach in most efficient way possible whereas index-based buying values audience composition ratios

Finding the metrics that matter for your product

  1. “Where most startups trip up is they don’t know how to ask the right questions before they start measuring.”
  2. Heart Framework Questions:
    • If we imagine an ideal customer who is getting value from our product, what actions are they taking?
    • What are the individual steps a user needs to take in our product in order to achieve a goal?
    • Is this feature designed to solve a problem that all our users have, or just a subset of our users?
  3. Key points in a customer journey:
    1. Intent to use: The action or actions customers take that tell us definitively they intend to use the product or feature.
    2. Activation: The point at which a customer first derives real value from the product or feature.
    3. Engagement: The extent to which a customer continues to gain value from the product or feature (how much, how often, over how long a period of time etc).

A Beginners Guide to Finding the Product Metrics That Matter

  1. It’s actually hard to find what metrics that matter, and there’s a trap of picking too many indicators
  2. Understand where your metrics fall under, eg. the HEART framework: Happiness, Engagement, Adoption, Retention, Task Success
  3. Don’t measure all you can and don’t fall into the vanity metrics trap, instead examples of good customer-oriented metrics:
    • Customer retention
    • Net promoter score
    • Churn rate
    • Conversions
    • Product usage
    • Key user actions per session
    • Feature usage
    • Customer Acquisition costs
    • Monthly Recurring Revenue
    • Customer Lifetime Value

Algorithms and Data Structures

Intro to Algorithms

  • Algorithm steps a program takes to complete a task – the key skill to derive is to be able to identify which algorithm or data structure is best for the task at hand
  • Algorithm:
    • Clearly defined problem statement, input, and output
    • Distinct steps need to be a specific order
    • Should produce a consistent result
    • Should finish in finite amount of time
  • Evaluating Linear and Binary Search Example
  • Correctness
    • 1) in every run against all possible values in input data, we always get output we expect
    • 2) algorithm should always terminate
  • Efficiency:
  • Time Complexity: how long it takes
  • Space Complexity: amount of memory taken on computer
  • Best case, Average case, Worst case

Efficiency of an Algorithm

  • Worst case scenario/Order of Growth used to evaluate
  • Big O: Theoretical definition of a complexity of an algorithm as a function of the size O(n) – order of magnitude of complexity
  • Logarithmic pattern: in general for a given value of n, the number of tries it takes to find the worst case scenario is log of n + 1 or O(log n)
  • Logarithmic or sublinear runtimes are preferred to linear because they are more efficient

Google Machine Learning Crash Course

Reducing Loss

  • Update model parameters by computing gradient – negative gradient tells us how to adjust model parameters to reduce lost
  • Gradient: derivative of loss with respect to weights and biases
  • Take small (negative) Gradient Steps to reduce lost known as gradient descent
  • Neural nets: strong dependency on initial values
  • Stochastic Gradient Descent: one example at a time
  • Mini-Batch Gradient Descent: batches of 10-1000 – losses and gradients averaged over the batch
  • Machine learning model gets trained with an initial guess for weights and bias and iteratively adjusting those guesses until weights and bias have the lowest possible loss
  • Convergence refers to when a state is reached during training in which training loss and validation loss change very little or not with each iteration after a number of iterations – additional training on current data set will not improve the model at this point
  • For regression problems, the resulting plot of loss vs. w1 will always be convex
  • Because calculating loss for every conceivable value of w1 over an entire data set would be an inefficient way of finding the convergence point – gradient descent allows us to calculate loss convergence iteratively
  • The first step is to pick a starting value for w1. The starting point doesn’t matter so many algorithms just use 0 or a random value.
  • The gradient descent algorithm calculates the gradient of the loss curve at the starting point as the vector of partial derivatives with respect to weights. Note that a gradient is a vector so it has both a direction and magnitude
  • The gradient always points in the direction of the steepest increase in the loss function and the gradient descent algorithm takes a step in the direction of the negative gradient in order to reduce loss as quickly as possible
  • The gradient descent algorithm adds some fraction of the gradient’s magnitude to the starting point and steps and repeats this process to get closer to the minimum
  • Gradient descent algorithms multiply the gradient by a scalar learn as the learning rate/step size
    • eg. If gradient magnitude is 3.5 and the learning rate is .01, the gradient descent algorithm will pick the next point .025 away from the previous point
  • Hyperparameters are the knobs that programmers tweak in machine learning algorithms. You want to pick a goldilocks learning rate, learning rate too small will take too long, too large, the next point will bounce haphazardly and could overshoot the minimum
  • Batch is the total number of examples you use to calculate the gradient in a single iteration in gradual descent
  • Redundancy becomes more likely as the batch size grows and there are diminishing returns in after awhile in smoothing out noisy gradients
  • Stochastic gradient descent is a batch size one 1 – a single example. With enough iterations this can work but is super noisy
  • Mini-batch stochastic gradient descent: compromise between full-batch and SGD, usually between 10 to 1000 examples chosen at random, reduces the noise more than SGD but more effective than full batch

First Steps with Tensorflow

  • Tensorflow is a framework for building ML models. TFX provides toolkits that allows you construct models at your preferred layer of abstraction.
  • The estimator class encapsulates logic that builds a TFX graph and runs a TF session graph, in TFX is a computation specification – nodes in graph represent operations. Edges are directed and represent the passing of an operation (a Tensor) as an operand to another operation






Jul-Dec Learning

What’s This?


Books Read:

So Good They Can’t Ignore You

Key Points:

  • It’s not about passion – it’s about gaining career capital so you have more agency over a career you want.
  • Control traps 1) you don’t have enough career capital to do what you want 2) employers don’t want you to change/advance/slowdown because you have skills valuable to them
  • Good jobs have autonomy, financial viability, and mission – you can’t get there on passion alone.
  • Figure out if the market you wish to succeed in is winner-take-all, one killer skill, eg. screenwriting is all about just getting a script read or auction-based, diverse collection of skills, eg. running a complex business.
  • Make many little bets and try different things that give instant feedback to see what is working or not and show what you’re doing in a venue that will get you noticed.
  • On Learning
    • Research bible-routine – summarize what you might work on – description of result and strategies used to do it.
    • Hour-tally and strain – just work on for an hour and keep track of it
    • Theory-Notebook – brainstorm notebook that you deliberately keep track of info in
    • Carve out time to research and independent projects
  • “Working right trumps finding the right work” p228
  • Good visual summary

The Manager’s Path

Key Points:

  • “Your manager should be the person who shows you the larger picture of how your work fits into the team’s goals, and helps you feel a sense of purpose in the day-to-day work”
  • “Developing a sense of ownership and authority for your work and not relying for manager to set the tone”
  • “Especially as you become more senior, remember that your manager expects you to bring solutions, not problems”
  • “Strong engineering managers can identify the shortest path through the systems to implement new futures”
  • Dedicate 20% of time in planning meetings to sustainability work
  • “Be carefully that locally negative people don’t stay in that mindset on your team for long. The kind of toxic drama that is created by these energy vampires is hard for even the best manager to combat. The best defense is a good offense in this case”
    • You are not their parent – treat them as adults and don’t get emotionally invested in every disagreement they have with you personally.


What is a predicate pushdown? In mapreduce

  1. Concept is if you issue a query to run in one place you’d spawn a lot of network traffic, making that query slow and costly. However, if yo updush down parts of the query to where data is stored and thus filter out most of the data, you reduce network traffic.
  2. You filter conditions as True or False – predicates, and pushdown query to where the data resides
  3. For example, you don’t need to pass through every single column for every map reduce job in the pipeline for no reason so you filter so you don’t read the other columns

What is a predicate pushdown?

  1. The basic idea is to push certain parts of SQL queries (the predicates) to where the data lives to optimize the query by filtering out data earlier rather than later so it skips reading entire files or chunks of files to reduce network traffic/processing time
  2. This is usually done with a function that returns a boolean in the where cause to filter out data
  3. Eg example below where clause “WHERE a.country = ‘Argentina’”
  table1 a
  table2 b ON a.id = b.id
  a.country = 'Argentina';

The Leaders Calendar

  1. 6 hours a day of non-work time, half with family and some downtime with hobbies
  2. Setting norms and expectations with e-mail is essential. For example sending e-mails from CEO late at night sets a wrong example for the company or CEO’s time is spend cc’d on endless irrelevant items.
  3. Be agenda-driven to optimize limited time and also not only let the loudest voices stand out so that the important work can get done, not just the work that appears the most urgent be work on strategy.
    1. A key way to do this is to limit routine activities that can be given to a direct report

What People Don’t Tell You About Product Management

  1. “Product Management is a great job if you like being involved in all aspects of a product — but it’s not a great job if you want to feel like a CEO.”
    1. You don’t necessary make the strategy, have resources, and have the ability to fire people. Your job is to get it done by being resourceful and convincing.
  2. Product Managers should channel the needs to the customer and follow a product from conception, dev, launch, and beyond. Be a cross functional leader coordinating between R&D, Sales, Marketing, Manufacturing, and Operations. Leadership and coordination are key. Your job is to make strategy happen and convincing people you work with.
  3. “For me, product management is exciting and stressful for the same reason: there’s unpredictability, there’s opportunity to create something new (which also means it may be unproven), and you’re usually operating with less data than you’d like, and everything is always a little bit broken.”

Web Architecture 101

  1. In web dev you almost always want to scale horizontally, meaning you add more machines into your pool of resources, versus vertically, meaning scaling by adding more powers (eg. CPU, RAM) to an existing machine, this redundancy allows you to have another plan so your applications keep running if a server goes down and makes your app more fault tolerant. You can also minimally couple different parts of the app backend to run on different servers.
  2. Job queues store lists of jobs that need to be run asynchronously – eg Google does not search the entire internet every time you do a search, it crawls the web asynchronously and updates search indexes along the way
  3. Typical data pipeline: firehouse that provides streaming interface to ingest and process data (eg. Kinesis and Kafka) -> raw data as well as final transformed/augmented data saved to cloud storage (eg. S3) -> data loaded into a data warehouse for analysis (eg. Redshift)

Running in Circles – Why Agile Isn’t Working and What We Do Differently

  1. “People in our industry think they stopped doing waterfall and switched to agile. In reality they just switched to high-frequency waterfall.”
  2. “Only management can protect attention. Telling the team to focus only works if the business is backing them up.”
  3. Think of software development as going uphill when you’re finding out the complexity/uncertainty and then downhill when you have certainty.

Product Managers – You Are Not the CEO of Anything

  1. Too many product managers think their role is that of an authoritarian CEO (with no power) and often disastrous because they think they have all the answers.
  2. You gain credibility through your actions and leadership skills.
  3. Product management is a team sportafter all, and the best teams don’t have bosses – they have coaches who ensure all the skills and experiences needed are present on the team, that everyone is in the right place, knows where the goal is, and then gets out of the way and lets the team do what they do best in order to reach that goal.”

Product Prioritization: How Do You Decide What Belongs in Your Product?

  1. Radical vision with this mad lips template Today, when [customer segment]want to [desirable activity/ outcome], they have to [current solution] . This is unacceptable, because [shortcomings of current solutions]. We envision a world where [shortcomings resolved]. We are bringing this world about through [basic technology/ approach].
  2. Four components to good product strategy
    1. Real Pain Points means “Who is it for?” and “What is their pain point?”
    2. Designrefers to “What key features would you design into the product?” and “How would you describe your brand and voice?”
    3. Capabilitiestackles the “Why should we be the ones doing it?”and “ What is our unique capability?”
    4. Logisticsis the economics and channels, like “What’s our pricing strategy?” and “What’s the medium through which we deliver this?”
  3. Then prioritize between sustainable and good fit

To Drive Business Success Implement a Data Catalog and Data Inventory

  • Companies have a huge gap between knowing where the data is located simply and what to do with it
  • Three types of metadata
    • Business Metadata: Give us the meaning of data you have in a particular set
    • Technical Metadata: Provide information on the format and structure of data – databases, programming envs, data modeling tools natively available
    • Operational Metadata: Audit trail of information of where the data came from, who created it, etc.
  • “Unfortunately, according to Reeve, new open source technologies, most importantly Hadoop, Hive, and other open source technologies do not have inherent capabilities to handle, Business, Technical AND Operational Metadata requirements. Firms cannot afford this lack as they confront a variety of technologies for Big Data storage, noted Reeve. It makes it difficult for Data Managers to know where the data lives.” http://www.dataversity.net/drive-business-success-implement-data-catalog-data-inventory/

Why You Can’t be Data Driven Without a Data Catalog

  1. A lot of data availability in organizations is “tribal knowledge” which severly limits the impact data has in an organization. Data catalogs should capture tribal knowledge
  2. Data catalogs need to work to have common definitions of important concepts like customer, product, and revenue, especially since different divisions actually will think of those concepts differently.
  3. A solution that one company did was a Looker-power integrated moel with a GitBook data dictionary.

What is a data catalog?

  1. At its core, a data catalog centralizes metadata. “The difference between a data catalog and a data inventory is that a data catalog curates the metadata based on usage.”
  2. Different types of data catalog users falls into three buckets
    1. Data Consumers – data and business analysts
    2. Data Creators – data architects and database engineers
    3. Data Curators – data stewards and data governors
  3. A good data catalog must
    1. Centralize all info on data in one place – structure, quality, definitons, and usages
    2. Allow users to self-service
    3. Auto-populate consistency and with accuracy

Why You Need a Data Catalogue and How to Select One

  1. “A good data catalog serves as a searchable business glossary of data sources and common data definitions gathered from automated data discovery, classification, and cross-data source entity mapping. Automated data catalog population is done via analyzing data values and using complex algorithms to automatically tag data, or by scanning jobs or APIs that collect metadata from tables, views, and stored procedures.”
  2. Should foster search and reuse of existing data in BI tools
  3. Should almost be an open platform where many people can use to see what they want to do with that

10 Tips to Build a Successful Data Catalog

  1. Who – understand the owner or trusted steward for asset
  2. What – aim to for a basic description of an asset as a minimum: business terminology, report functionality, and basic purpose of a dataset
  3. Where – where the underlying assets are

The Data Catalog – A Critical Component to Big Data Success

  1. Most data lakes do not have effective metadata management capabilities that make using them inefficient
    1. Need data access security solutions (role and asset), audit trails of update and access, and inventory of assets (technical and business metadata)
  2. First step is to inventory existing data and make it usable at a data store level – table, file, database, schema, server, or directory
  3. Figure out how to ingest new data in a structure manner, eg. data scientist wants to incorporate new data in modeling

Data Catalog for the Enterprise – What, Why, & What to look for?

  1. With the growth of enormous data lakes, data sets need to be discovered, tagged, and annotated
  2. Data catalogs can also eliminate database duplicity
  3. Challenges of implementing data catalogs include educating org on the value of a single source of data, dealing with tribalism, and

Bridging the gap: How and why product management differs from company to company

  1. NYC vs SF product management disciplines are different due to key ecosystem factors: NYC driven by tech enhancing existing industries and thus sales driven while Bay Area creates entire new categories: vision and collaboration driven. NYC more stable exits but less huge ones
  2. This dichotomy in product management approaches is due to how to bring value to different markets
  3. Successful product managers need six key ingredients
    1. Strategic Thinking
    2. Technical proficiency
    3. Collaboration
    4. Communication
    5. Detail Orientation
    6. User Science

Treehouse Learning

Rest API Basics

  • REST (Representational State Transfer) is really just another layer of meaning on top of HTTP
  • API provides a programmatic interface, a code UI basically, to that same logic and data model. Communication is done through HTTP and burden on creating interface is on users of API, not the creator
  • Easy way to say it – APIs provide code that makes things outside of our application easier to interact inside of our application
  • Resources: usually a model in your application -> these retrieved, created, or modify in API in endpoints representing collections of records
    • api/v1/players/567
  • Client request types to API:
    • GET is used for teching either a collection of resources or single resource.
    • POST is used to add a new resource to a collection, eg. POST to /games to create a new game
    • PUT is HTTP method we use when we want to update a record
    • DELETE is used for sending a DELETE request to a detail record, a URL for a single record, or just deleting that record
  • Requests
    • We can use different aspects of the requests to change the format of our response, the version of the API, and more.
    • Headers make transactions more clear and explicit, eg. Accept (specifies file format requester wants), Accept-Language, Cache-Control
  • Responses
    • Content-Type: text/javascript – > header to specify what content we’re sending
    • Other headers include Last-Modified, Expired, and Status (eg. 200, 500, 404)
    • 200-299 content is good and everything is ok
    • 300-399 request was understood but the requested resource is now located somewhere else. Use these status codes to perform redirects to URLs most of the time
    • 400-499 Error codes, eg wrongly constructed or 404 resource no longer exists
    • 500-599 Server End errors
  • Security
    • Cache: usually a service running in memory that holds recently needed results such as a newly created record or large data se. This helps prevent direct database calls or costly calculations on your data.
    • Rate Limiting: allowing each user only a certain number of requests to our API in a given period to prevent too many requests or DDOS attacks
    • A common authentication method is the use of API toekns – you give your users a token and secret key as a pair and they use those when they make requests to your server so you know they are who they say are.

Planning and Managing the Database

  • Data Definition Language – language that’s used to defined the structure of a database

When Object Literals Are Not Enough

  • Use classes instead of object literals to not repeat so much code over and over again
  • Class is a specification, a blueprint for an object that you provide with a base set of properties and methods
  • In a constructor method, this refers to the object that is being created, which is why it’s the keyword here.

Google Machine Learning Course (30% through highlights)

ML – reduces time programming

  • Scales making sense of data
  • Makes projects customizable much more easily
  • Let’s you solve programming problems that humans can’t do but algos do well
  • Use stats and not logic to solve problems, flips the programming paradigm a bit

Label is the thing we’re picking, eg. Y in linear regression
Features are Xs or way we represent our data, an input variable
– eg. header, words in e-mail, to and from addresses, routing info, time of day
Example: particular instance of data, x, eg. an email

Labeled example has { features, label}: (x, y) – used to train model ( email, spam or not spam)
Unlabeled examples has {features, ?}: (x, ?) – used for making predictions on new data (email, ?)
Model: thing that does predicting. Model maps examples to predicted labels: y’ – defined by internal parameters, which are learned

Framing: What is Supervised Machine Learning? Systems learn to combine input to product useful predictions on never before seen data
* Training means creating or learning the model.
* Inference means applying the trained model to unlabeled examples to make useful predictions (y’)
* Regression models predict continuous values: eg. value of house, probability user will click on an head
* Classification model: predicts discrete values, eg. is the given e-mail message spam or not spam? Is this an image of a dog, cat, or hampster?

Descending into ML
y = wx + b

w refers for weight vectors, gives slope

b gives bias

Loss: loss means how well line is predicting example, eg. distance from line
* loss is on a 0 through positive scale
* Convenient way to define loss for linear regression
* L2Loss also known as squared error = square of difference between prediction and label (observation – prediction)2 = (y-y’)2
* We care about minimizing loss all across datasets
* Measure of how far a model’s predictions are from its label – a measure of how bad the model is

Feature is an input variable of x value – something we know

Bias: b or An intercept or offset from an origin. Bias (also known as the bias term) is referred to as b or w0 in machine learning models.

Inference: process of making predictions by applying trained models to unlabeled examples. In statistics, inference refers to the process of fitting the parameters of a distribution conditioned on some observed data

Unlabeled example
An example that contains features but no label. Unlabeled examples are the input to inference. In semi-supervised and unsupervised learning, unlabeled examples are used during training.

Logistic regression
Model that generates probability for each possible discrete label value in classification problems by applying a sigmoid function to a linear prediction. Can be used for binary or multi-class classifications

Sigmoid function
function that maps logistic or multinomial regression output (log odds) to probabilities, returning a value between 0 and 1. Sigmoid function converts variance

K-means: clustering algorithm from signal analysis

Random Forest
Ensemble approach to finding decision tree the best fits training data by creating many decision trees and then determining the average – the random part of the term refers to building each of the decision trees from a random selection of features, the forest refers to the set of decision trees

Coefficient for a feature in a linear model or edge in a deep network. Goal of training a linear model is to determine the ideal weight for each feature. If a weight is 0, then its corresponding feature does not contribute to the model

Mean squared error (MSE) average squared loss per data set -> sum squared losses for each individual examples and divide by # examples

Although MSE is commonly-used in machine learning, it is neither the only practical loss function nor the best loss function for all circumstances.

empirical risk minimization (ERM): Choosing the function that minimizes loss on the training set.

sigmoid function: A function that maps logistic or multinomial regression output (log odds) to probabilities, returning a value between 0 and 1. In other words, the sigmoid function converts sd from logistic regression into a probability between 0 and 1.

binary classification: classification task that outputs one of two mutually exclusive classes, eg. hot dog not a hot dog

Logistic Regression
* Prediction method that gives us probability estimates that are calibrated
* Sigmoid something that gives bounded value between 0 and 1
* useful for classification tasks
* regularization important as model will try to drive losses to 0 and weights may go crazy
* Linear Logistic Regression is fast, efficient to train, and efficient to make predictions and scales to massive data and good for low latency data
* A model that generates a probability for each possible discrete label value in classification problems by applying a sigmoid function to a linear prediction. Although logistic regression is often used in binary classification problems, it can also be used in multi-class classification problems (where it becomes called multi-class logistic regression or multinomial regression).

Many problems require a probability estimate as output. Logistic regression is an extremely efficient mechanism for calculating probabilities. Practically speaking, you can use the returned probability in either of the following two ways:
* “As is”
* Converted to a binary category.

Suppose we create a logistic regression model to predict the probability that a dog will bark during the middle of the night. We’ll call that probability:
* p(bark | night)
* If the logistic regression model predicts a p(bark | night) of 0.05, then over a year, the dog’s owners should be startled awake approximately 18 times:
* startled = p(bark | night) * nights
* 18 ~= 0.05 * 365

In many cases, you’ll map the logistic regression output into the solution to a binary classification problem, in which the goal is to correctly predict one of two possible labels (e.g., “spam” or “not spam”).

Early Stopping
* Regularization method the ends model before training loss finishes decreasing. You end when loss on validation dataset starts to increase

Key takeaways:
* Logistic regression models generate probabilities. In order to map a logistic regression value to a binary category, you must define a classification threshold (also called decision threshold), eg the value where you can categorize something as hotdog not a hotdog. (Note: Tuning a threshold for logistic regression is different from tuning hyperparameters such as learning rate)
* Log Loss is the loss function for logistic regression.
* Logistic regression is widely used by many practitioners.

* We can use logistic regression for classification by using fixed thresholds for probability outputs, eg, it’s spam if it exceeds .8.

You can evaluate classification performance by
* Accuracy: fraction of predictions we got right but has key flaws, eg. if there are class imbalances, such as when positives and negatives are extremely rare for example predicting CTRs. You can have model no features but a bias a feature that causes it ti predict false always that would be highly accurate but has no value

Better is to look are True Positives and False Positives
* True Positives: Correctly Called
* False Positives: Called but not true
* False Negatives: Not predicted and it happened
* True Negatives: Not called and did not happen
* A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class.
* A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class.

Precision: True positive/all positive predictions, how precisely was positive class right
Recall: True Positives/All Actual Positives: out of all the possible positives, how many did the model correctly identify
* If you raise classification threshold, reduces false positives and raises precision
* We might not know in advance what best classification threshold is – so we evaluate across many possible classification thresholds – this is ROC curve

Prediction Bias
* Sum of all these we predict to all things we observe
* ideally – average of predictions == average of observations
* Logistic predictions should be unbiased
* Bias is a canary, zero bias does not mean all is good but it’s a sanity check. Look for bias in slices of data to guide improvements and debug model

Watch out for class imbalanced sets, where there a significant disparity between the number of positive and negative labels. Eg. 91% accurate predictions but only 1 TP and 8 FN, eg. 8 out of 9 malignant tumors end up undiagnosed.

Calibration Plots Showed Bucketed Bias
* Mean observation versus mean prediction

Precision = TP/(TP + FP) number of labels correctly classified
Recall = TP/(TP + FN) = how many actual positives were identified correctly, attempts to answer the question, how many of the actual positives was identified correctly?
* To evaluate the effectiveness of models, you must examine both precision and recall which are often in tension because improving precision typically reduces recall and vice versa.
* When you increase the classification threshold, then number of false positives decrease, but false negatives increase, so precision increases while recall decreases.
* When you decrease the classification threshold, false positives increase and false negatives negatives decrease, so recall increase while precision decreases.
* eg. If you have a model with 1 TP and 1 FP = 1/(1+1) = precision is 50% and when it predicts a tumor is malignant, it is correct 50% of the time

Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instance
* Suppose a computer program for recognizing dogs in photographs identifies 8 dogs in a picture containing 12 dogs and some cats. Of the 8 identified as dogs, 5 actually are dogs (true positives), while the rest are cats (false positives). The program’s precision is 5/8 while its recall is 5/12. When a search engine returns 30 pages only 20 of which were relevant while failing to return 40 additional relevant pages, its precision is 20/30 = 2/3 while its recall is 20/60 = 1/3. So, in this case, precision is “how useful the search results are”, and recall is “how complete the results are”.
* In an information retrieval scenario, the instances are documents and the task is to return a set of relevant documents given a search term; or equivalently, to assign each document to one of two categories, “relevant” and “not relevant”. In this case, the “relevant” documents are simply those that belong to the “relevant” category. Recall is defined as the number of relevant documents retrieved by a search divided by the total number of existing relevant documents, while precision is defined as the number of relevant documents retrieved by a search divided by the total number of documents retrieved by that search.
* In information retrieval, a perfect precision score of 1.0 means that every result retrieved by a search was relevant (but says nothing about whether all relevant documents were retrieved) whereas a perfect recall score of 1.0 means that all relevant documents were retrieved by the search (but says nothing about how many irrelevant documents were also retrieved).

* Receiver Operating Characteristics Curve
* Evaluate every possible classification threshold and look at true positive and false positive rates
* Area under that curve has probabilistic interpretation
* If we pick a random positive and random negative, what’s the probability my model ranks them in the correct order – that’s equal to area under ROC curve

Gives aggregate measure of performance aggregated across all possible classification thresholds
TP Rate X-axis FP Rate Y-Axis
AUC = area under curve
* Probably that model ranks a random positive example more highly than a random negative example:
* One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.

Characteristics of AUC to note:
* AUC is scale-invariant. It measures how well predictions are ranked, rather than their absolute values. Note: this is not always desireable: sometimes we really do need well calibrated probability outputs, AUC does not provide that
* AUC is classification-threshold-invariant. It measures the quality of the model’s predictions irrespective of what classification threshold is chosen.
* Classification-threshold invariance is not always desirable. In cases where there are wide disparities in the cost of false negatives vs. false positives, it may be critical to minimize one type of classification error. For example, when doing email spam detection, you likely want to prioritize minimizing false positives (even if that results in a significant increase of false negatives). AUC isn’t a useful metric for this type of optimization.
Logistic regression predictions should be unbiased.
* That is: “average of predictions” should ≈ “average of observations”. Good models should have near-zero bias.
* Prediction bias is a quantity that measures how far apart those two averages are. That is:
* prediction bias = average number of predictions – average of labels in data set
* Note: “Prediction bias” is a different quantity than bias (the b in wx + b)

A significant nonzero prediction bias tells you there is a bug somewhere in your model, as it indicates that the model is wrong about how frequently positive labels occur.
* For example, let’s say we know that on average, 1% of all emails are spam. If we don’t know anything at all about a given email, we should predict that it’s 1% likely to be spam. Similarly, a good spam model should predict on average that emails are 1% likely to be spam. (In other words, if we average the predicted likelihoods of each individual email being spam, the result should be 1%.) If instead, the model’s average prediction is 20% likelihood of being spam, we can conclude that it exhibits prediction bias.
* Causes are: incomplete feature set, noisy data set, buggy pipeline, biased training sample, overly strong regularization

You might be tempted to correct prediction bias by post-processing the learned model—that is, by adding a calibration layer that adjusts your model’s output to reduce the prediction bias. For example, if your model has +3% bias, you could add a calibration layer that lowers the mean prediction by 3%. However, adding a calibration layer is a bad idea for the following reasons:
* You’re fixing the symptom rather than the cause.
* You’ve built a more brittle system that you must now keep up to date.
* If possible, avoid calibration layers. Projects that use calibration layers tend to become reliant on them—using calibration layers to fix all their model’s sins. Ultimately, maintaining the calibration layers can become a nightmare.

Apr-June 2018 Learning

Books Read (related to work/professional development/betterment):


Giving meaning to 100 billion analytics events a day

  1. Tracking events were sent by browser over HTTP to a dedicated component and enqueues them in a Kafka topic. You can build a Kafka equivalent in BigQuery to use as Data Warehouse system
  2. ‘When dealing with tracking events, the first problem you face is the fact that you have to process them unordered, with unknown delays.
    1. The difference between the time the event actually occurred (event time) and the time the event is observed by the system (processing time) ranges from the millisecond up to several hours.’
  3. The key for them was finding ideal batch duration

What is a Predicate Pushdown?

  1. Basic idea is certain parts of SQL queries (the predicates) can be “pushed” to where the data lives and reduces query/processing time by filtering out data earlier rather than later. This allows you to optimize your query by doing things like filtering data before it is transferred over a network, loading into memory, skipping reading entire files or chunks of files.
  2. ‘A “predicate” (in mathematics and functional programming) is a function that returns a boolean (true or false). In SQL queries predicates are usually encountered in the WHEREclause and are used to filter data.’
  3. Predicate pushdowns filters differently in various query environments, eg Hive, Parquet/ORC files, Spark, Redshift Spectrum, etc.

I hate MVPs. So do your customers. Make it SLC instead.

  1. Customers hate MVPS, too M and almost never V – simple, complete, and lovable is the way to go
  2. The success loop of a product “is a function of love, not features”
  3. “An MVP that never gets additional investment is just a bad product. An SLC that never gets additional investment is a good, if modest product.”

Documenting for Success

  1. Keeping User Stories Lean and Precise with:
    1. User Story Objectives
    2. Use Case Description
    3. User Interaction and Wireframing
    4. Validations
    5. Communication Scenarios
    6. Analytics
  2. Challenges
    1. Lack of participation
    2. Documentation can go sour
  3. Solutions
    1. Culture – tradition of open feedback
    2. Stay in Touch with teams for updates
    3. Documentation review and feedback prior to sprint starts
    4. Track your documents

WTF is Strategy

  1. Strategic teaming is what sets apart seniors from juniors
  2. Strategy needs
    1. Mission: Problem you’re trying to solve and who for
    2. Vision: Idealized solution
    3. Strategy: principles and decisions informed by reality and caveated with assumptions that you commit to ahead of dev to ensure likelihood of success in achieving your vision
    4. Roadmap: Concreate steps
    5. Execution: Day-today activities
  3. “Strategy represents the set of guiding principles for your roadmapping and execution tasks to ensure they align with your mission and vision.”

Corporate Culture in Internet Time

  1. “”The dirty little secret of the Internet boom,” says Christopher Meyer, who is the author of “Relentless Growth,” the 1997 management-based-on-Silicon-Valley-principles book, “is that neither startup wizards nor the venture capitalists who fund them know very much about managing in the trenches.”
  2. “ The most critical factor in building a culture is the behavior of corporate leaders, who set examples for everyone else (by what they do, not what they say). From this perspective, the core problem faced by most e-commerce companies is not a lack of culture; it’s too much culture. They already have two significant cultures at play – one of hype and one of craft.”
  3. Leaders need to understand both craft and hype cultures since they have to rely on teams that come from both to deliver. They need to set-up team cultures and infrastructure that supports inter-team learning.

Do You Want to Be Known For Your Writing, or For Your Swift Email Responses? Or How the Patriarchy has fucked up your priorities

  1. Women are conditioned to keep proving themselves – our value is contingent on ability to meet expectation of others or we will be discredited. This is often true, but do you want to a reliable source of work or answering e-mails?
  2. Stop trying to get an A+ in everything, it’s a handicap in making good work. “Again, this speaks most specifically to women, POC, queers, and other “marginalized” folks. I am going to repeat myself, but this shit bears repeating. Patriarchy (and institutional bigotry) conditions us to operate as if we are constantly working at a deficit. In some ways, this is true. You have to work twice as hard to get half the credit. I have spent most of my life trying to be perfect. The best student. The best dishwasher. The best waitress. The best babysitter. The best dominatrix. The best heroin addict. The best professor. I wanted to be good, as if by being good I might prove that I deserved more than the ephemeral esteem of sexist asshats.”

Listen to me: Being good is a terrible handicap to making good work. Stop it right now. Just pick a few secondary categories, like good friend, or good at karaoke. Be careful, however of categories that take into account the wants and needs of other humans. I find opportunities to prove myself alluring. I spent a long time trying to maintain relationships with people who wanted more than I was capable of giving

  1. Stop thinking no as just no but saying yes to doing your best work

Dear Product Roadmap, I’m Breaking Up with You

  1. A major challenge is setting up roadmap priorities without real market feedback, especially in enterprise software
  2. Roadmaps should be planned with assets in place tied closely to business strategy
    1. A clearly defined problem and solution
    2. Understanding of your users’ needs
    3. User Journeys for the current experience
    4. Vision -> Business Goals -> User Goals -> Product Goals -> Prioritize -> Roadmap
  3. Prioritization should be done through the following lens: feasibility, desirability, and viability

The 7 Steps of Machine Learning Google Video

  • Models are created via training
  • Training helps create accurate models that answers questions correctly most of the time
  • This require data to train on
    • Defined features for telling apart beer and wine could be color and alcohol percentage
  • Gathering data, quality and quantity determine how good model can be
  • Put data together and randomize so order doesn’t affect how that determines what is a drink for example
  • Visualize and analyze during data prep if there’s a imbalance in data in the model
  • Data needs to be split, most for (70-80%) and some left for evaluation to test accuracy (20-30%)
  • A big choice is choosing a model – eg some are better for images versus numerical -> in the beer or wine example is only two features to weigh
  • Weights matrix (m for linear)
  • Biases metric (b for linear)
  • Start with random values to test – creates iterations and cycles of training steps and line moves to split wine v beer where you can evaluate the data
  • Parameter tuning: How many times we through the set -> does that lead to more accuracies, eg learning rate how far we are able to shift each line in each step – hyperparameters are experimental process bit more art than science
  • 7 Steps: Gathering Data -> Preparing Data -> Choosing a Model -> Training -> Evaluation -> Hyperparameter Tuning -> Prediction

Qwik Start Baseline Infra Quest: 

  • Cloud Storage Google Consolae
  • Cloud IAM
  • Kubernetes Engine

Treehouse Learning:  

Javascript OPP

  • In JavaScript, state are represented by objects properties and behaviors are represented by object methods.
    • Radio that has properties like station and volume and methods like turning off or changing a station
  • An object’s states are represented by “property” and its behaviors are presented by “method.”
  • Putting properties and methods into a package and attaching it to a variable is called encapsulation.

Intro SQL Window Functions

  • Function available in some variations of SQL that lets you analyze a row in context of entire result set – compare one row to other rows in a query, eg percent of total or moving average

Common Table Expressions using WITH

  • CTE – a SQL query that you name and reuse within a longer query, a temporary result set
  • You place a CTE at the beginning of a complete query using a simple context
--- create CTES using the WITH statement
WTH cte_name AS (
  --- select query goes here

--- use CTEs like a table
SELECT * FROM cte_name
  • CTE name is like an alias for the results returned by the query, you can then use the name just like a table name in the queries that follow the CTE
WITH product_details AS (
  SELECT ProductName, CategoryName, UnitPrice, UnitsInStock
  FROM Products
  JOIN Categories ON PRODUCTS.CategoryID = Categories.ID
  WHERE Products.Discontinued = 0

SELECT * FROM product_details
ORDER BY CategoryName, ProductName
SELECT CategoryName, COUNT(*) AS unique_product_count, 
SUM(UnitsInStock) AS stock_count
FROM product_details
GROUP BY CategoryName
ORDER BY unique_product_count
  • CTE makes code more readable, organizes queries into reusable modules, you can combine multiple CTEs into a single query, it can better match of how we think of results set in the real world
    • all orders in past month-> all active customers -> all products and categories
    • Each would be a CTE
  • Subqueries create result sets that look just like a table that can be joined to another tables
WITH all_orders AS (
  SELECT EmployeeID, Count(*) AS order_count
  FROM Orders
  GROUP BY EmployeeID
late_orders AS (
    SELECT EmployeeID, COUNT(*) AS order_count
    FROM Orders
    WHERE RequiredDate <= ShippedDate
    GROUP BY EmployeeID
SELECT Employees.ID, LastName,
all_orders.order_count AS total_order_count,
late_orders.order_count AS late_order_count
FROM Employees
JOIN all_orders ON Employees.ID = all_orders.EmployeeID
JOIN late_orders ON Employees.ID = late_orders.EmployeeID
  • Remember one useful feature of CTES is you can reference them later in other CTEs, eg. revenue_by_employee below pulling from all_sales
  • You can only reference a CTE created earlier in the query, eg first CTE can’t reference the third
all_sales AS (
  SELECT Orders.Id AS OrderId, Orders.EmployeeId,
  SUM(OrderDetails.UnitPrice * OrderDetails.Quantity) AS invoice_total
  FROM Orders
  JOIN OrderDetails ON Orders.id = OrderDetails.OrderId
  GROUP BY Orders.Id
revenue_by_employee AS (
  SELECT EmployeeId, SUM(invoice_total) AS total_revenue
  FROM all_sales
  GROUP BY EmployeeID
sales_by_employee AS (
  SELECT EmployeeID, COUNT(*) AS sales_count
  FROM all_sales
  GROUP BY EmployeeID
SELECT revenue_by_employee.EmployeeId,
revenue_by_employee.total_revenue/sales_by_employee.sales_count AS avg_revenue_per_sale
FROM revenue_by_employee
JOIN sales_by_employee ON revenue_by_employee.EmployeeID = sales_by_employee.EmployeeID
JOIN Employees ON revenue_by_employee.EmployeeID = Employees.Id
ORDER BY total_revenue DESC

March 2018 Learning

Less than normal last month due to business travel



Agile Died While You Were Doing Your Standup

  1. Agile has been implemented poorly to enterprise wholesale by consultancies that mechanizes and dehumanizes teams and doesn’t respect the craft – causing them to deliver outputs instead of outcomes that drive values for customers
  2. The problem Product management, UX, engineer, dev-ops, and other core competencies need to be one team under one leader and give it autonomy and accountability to connect solving problems. If implemented correctly – it empowers teams to work toward shared outcomes with both velocity and accuracy.
  3. Embrace discovery – discovery data matched along shipped experiences creates real customer value and trust that teams can work autnomously with accountability and shipping something that meets both company and user objectives.


Avoiding the Unintended Consequences of Casual Feedback

  • Your seniority casts a shadow or the org, your casual feedback may be interpreted as a mandate – make sure it’s clear whether its opinion, strong suggestion, or mandate
    1. Opinion: “one person’s opinion” your title and authority should to enter into the equation
    2. Strong suggestion: falls short of telling team what to do – senior executive draws on experience but provides team to feel empowered to take risks. This is the difficult balance to strike and requires taming of egos to do what’s best – you also have to trust the people you’ve empowered to have the final say.
    3. Mandate: issue to avoid prohibitively costly mistakes – but too often without right justification signals a demotivating lack of trust


Ask Women in Product: What are the Top 3 things you look for when hiring a PM?

  1. Influence without authority – figuring out what makes you tick, your team, your customers. Read in between lines. How did you deal with past conflicts
  2. Intellectual curiosity- how did you deal with ambiguous problem or were intimidated
  3. Product sense – name compelling product experience you built
  4. Empathy – unmet needs and pain points – how would you design an alarm clock for the blind
  5. Product intuition – access product, feature, or user flow
  6. Listening and communication skills – read rooms for implicit and explicit


Why Isn’t Agile Working?

  1. Waiting time isn’t addressed properly
  2. Doesn’t account well for unplanned work, multitasking, and impacts from shared services
  3. Even though dev goes faster in agile, it has no bearing on making the right product decisions and working to realize benefits. Agile is useful when it services as a catalyst for continuous improvement and the rest of the org structure is in line – eg. DevOps, right management culture, incremental funding v project-based funding, doing less and doing work that matters, looking at shared services, mapping value streams, etc.


Treehouse Learning:  

Changing object literal in dice rolling application into constructor function that takes in the number of sites as an argument. Each instance created calls the method for running the base.

function Dice(sides) {

            this.sides = sides;
            this.roll = function() {

                        var randomNumber = Math.floor(Math.random() * this.sides) +1;
                        return randomNumber;



var dice= new Dice(6) // new instance of 8 sided die


Watch out for applications running code again and again unnecessarily, like in code above. The JavasScript property prototype is like an object literal that can be added to roll property, when we assign a function to it, it becomes a method and is no longer needed in the constructor function. Prototypes can be used as templates for objects, meaning values and behavior can be shared between instances of objects.

Dice.prototype.roll = function diceRoll() {

            return randomNumber;

} // shared between all instances in template/prototype

function Dice(sides) {

            this.sides = sides;





Dec 2017 Learning

Less reading and off-time Treehouse learning this month. Want to timebox at least 10-15 minutes a day for these.

Treehouse AJAX Basics:

  • Make sure classes correspond with html
  • Use removeClass() say after something, like a button, is selected so not all the buttons are selected for example
  • Passing data to set-up API example:

  $('button').click(function () {



    var flickerAPI = "http://api.flickr.com/services/feeds/photos_public.gne?jsoncallback=?"; // adding JSON callback to query string

    var animal = $(this).text(); // this refers to button and text() gets text from html element

    var flickrOptions = {

      tags: animal,

      format: "json"


    function displayPhotos(data) {

      var photoHTML = '<ul>';

      $.each(data.items, function(i, photo) {

          photoHTML += '<li class="grid-25 tablet-grid-50">';

          photoHTML += '<a href="' + photo.link + '" class="image">';

          photoHTML += '<img src="' + photo.media.m + '"></a></li>';

      });// loop through the array applying the callbackfunction

      photoHTML += '</ul>';



    $.getJSON(flickerAPI, flickrOptions, displayPhotos); // three arguemnts, URL to resource, data we want to send with URL, callback function

  }); // function will run each time button is clicked


Treehouse UX Basics Tools UX-ers Use

  • Card Sorting: all different pieces of content on card and ask users to group the cards. Optimal Sort or Remote Search can be used to do remote.
  • Search Logs: understand what users are looking for
  • Content Inventories: Excel, etc. so there’s one way to look at all it
  • Beyond Philosophy defines UX as “an interaction between an organization and a customer as perceived through a customer’s conscious and subconscious mind. It is a blend of an organization’s rational performance, the sense stimulated, and the emotions evoked and intuitively measured against customer expectations across all moments of contact.”
  • Customer or User Journeys – Mapping out phases of customer’s journey and touchpoints. Identify opportunities, etc. through this process
  • Flow Diagrams: Steps user take
  • Wireframes: Diagrams or blueprints to show information relationships on pages and views
  • Comps: Showing details of specific moment of context. Think wireframes + aesthic
  • Prototypes: Show working relationships, aesthetic, and interactivity
  • Usability Testing
    • Moderating means there’s a facilitator asking questions and assigning questions
    • Unmoderated: puts together tasks while users go on their own

Treehouse UX Basics: Strategic UX

  • UX as a strategic initiative: see it at organizational or strategic level rather than immediate goals for users and see how important a task is to overall company, eg. how does getting auto quotes impact overall org’s bottom line?
  • UXers and non-UXers alike don’t agree on how to define UX
  • Your value is partnering with business and technology to emphasize with users and creates better experience and better user loyalty that brings more to bottom line
  • Selling UX means 1) Understanding what your business and technology partners value 2) Describe to them how UX meets those values in tailored responses to them by finding root causes, eg. what are the roots of wanting conversion rates. Don’t explain hows of UX but the Whys so you’re not an expense but a necessity

How to Build an Engineering Culture that Focuses on Impact

  1. Share with the engineers the value they’re creating, even if it’s “grungy but critical tasks” to let them know they’re being valued at the company
  2. Daniel Pink argues motivation comes from three key elements: autonomy, mastery, and purpose
  3. “Shape your culture through conversations and stories,” simply writing values doesn’t really mean anything

13 tips for product leaders on distributed teams

  1. Have an insider on your leadership team that can bridge cultural gaps and understand context of both languages and cultures and can mentor colleagues on both sides when it comes to improving communication
  2. Geographic gaps can multiply specialization gaps, eg business versus R&D that is compounded with distance and cultural differences
  3. If your engineers and business people look down on each other it’s your fault, create transparency and appreciation: “It might seem irrelevant to show your messaging and positioning documents to engineering or to show complex technology architecture to your sales people, but trust me, people learn to appreciate the challenges of the different roles when you surface the complexity. Animosity among colleagues usually stems from a general lack of understanding. Let members of each team shine and teams will show each other more support and respect.”

Three questions to ask yourself, before speaking to your users

  1. “What do you need to learn?”
    1. The big picture questions: who are customers, what’s their biggest problem, what do they want?
  2. “What do we need to learn right now to make progress?”
    1. Outcome of research that immediate action can be taken on
  3. “What’s the best way to learn?”
    1. Focus on doing minimum effort or method (interview versus user test) to learn

pm@olin Metrics (Class 8)

Continuous Improvement

  • PMs can create detailed factual timeline for post mortems – everyone should add what’s missing and note patterns as well as things done well and things that can improve on next time

2017 List of Wins + 2018 Look Ahead

I thought I was going to have a low-key NYE alone, but ended up going out. I still read this before, and thought it’d be good to put down a list of #wins and just things I’m thankful for this year and looking ahead for 2018.


  1. New role at work and working on an interesting project with a team. Felt like my career moved ahead a lot after feeling a bit stalled.
  2. I read 45 books last year, most captured here on Goodreads 
  3. I lose 11 lbs without really grinding hard, just more positive lifestyle choices
  4. Saved nearly 25% of my income
  5. Figured out who and what to prioritize in my life

For Next Year:

  1. Improve on my process at work as well as attitude and results. At my current workplace, I think we have an opportunity to build a good culture – so I hope that’s something I can help do.
  2. Read more fiction this year and still definitely read 30 books as a goal
  3. Continue to go on this healthier lifestyle path (incorporating yoga into my weekly workout routine, and eating more vegetarian meals namely)
  4. Save 30% of income
  5. Actualize some travel and academic goals

Oct Learning

Just my “three key points” notes from various reading I thought was work helpful this month:

PSFK Advertising Playbook Overview

  1. Experiential marketing now is the most critical tool
  2. Shift from ads to customer relationships and decline of online ads
  3. Emotional connections realign brands -> engineered enjoyment, contextual calibration, and third space communities are opportunities


Knowns vs Unknowns — Are you building a successful company or just typing?

  1. First known unknown is that you envision a product that solves a problem that a small group of users have
  2. Engineer’s primary job isn’t really writing code per se, but improving product for you users
  3. “What I often hear from CEOs is that “my CTO thinks we need to rebuild the backend so it’s scaleable.” The reality is that if you haven’t yet solved for the product’s scaleable and repeatable growth, you don’t know what the backend needs to be. If you’ve hired people that care more about the programming languages/frameworks and not the KPIs of your product, you’ll constantly have this internal battle. Remind them that writing software is the easy part. Building a company that scales isn’t.”

6 lessons learned about technical debts management in Silicon Valley

  1. Product always needs to be improved and have tech debts happening at once (80/20 rule)
  2. Top Down vision on the importance of these debts “It is not about the money you can make, it is about the money you won’t lose”
  3. Before you kill features, identify who are using it, find an alternative, and explain why you are killing a feature


  • “This is because the vast majority of people tend to play the middle—they focus on the vague minutiae that doesn’t matter”
  • Two things happen when you’re too focused on the middle:
    • You’re only successful to a certain level and then hit a plateau
    • You get stuck in one of two extremes: you get stuck either because you become too romantic on ideals and neglect the skills you need to execute or you get tied up in minutiae or politics and lose sight of the bigger picture.

Unit Economics

  1. “Unit economics are the direct revenues and costs associated with a particular business model expressed on a per unit basis.” Eg Lifetime Value, Customer Acquisition Cost (CPA)
  2. What you want to do as a product manager is increase average rev per user (ARPU), increase customer lifetime, and drive expansion revenue from existing cusotmers
  3. Make sure you know what your most profitable segment is and what their composite is of the user base

pm@olin: Buildiing (Class 5)

  1. Understand your personal work and productivity style
  2. Understand the style of your team and tailor your project management to the team – being cognizant of your personal style
  3. Understand your software processes (eg. Waterfall or Agile) and bug triage

Offshore Development: Pluses and Minuses for Product Managers

  1. Hard part is to learn and understand the team and learn what makes them tick and how you can leverage all this and control for issues such as different work cultures and different accents over conference phones
  2. Get to know them and make sure they know you
  3. Keep them informed, establish routines (especially communicating with remote team lead and holding them accountable, hold all-team meetings semi-frequently), and leverage tools

How we develop great PM / Engineering relationships at Asana

  1. Semi-formalized way for sharing leadership and credit
  2. Remember mantra product owns the problems and engineering owns solutions
  3. ‘Clarify roles and reinforce them with mutual respect’