Google's Machine Learning Crash Course - 43 Rules of Machine Learning (summary)
Tags: machinelearning, python, programming
Published: 2021-08-12 10:58:51

At the end of this article, you will understand the basic knowledge of machine learning and get the benefit of Google's best practices in machine learning:

This approach will work well for a long time. Diverge from this approach only when there are no more simple tricks to get you any farther.

Rule #1: Don’t be afraid to launch a product without machine learning

Rule #2: First, design and implement metrics.

Rule #3: Choose machine learning over a complex heuristic.

Rule #4: Keep the first model simple and get the infrastructure right.

Rule #5: Test the infrastructure independently from the machine learning.

Rule #6: Be careful about dropped data when copying pipelines.

Rule #7: Turn heuristics into features, or handle them externally.

Rule #8: Know the freshness requirements of your system.

Rule #9: Detect problems before exporting models.

Rule #10: Watch for silent failures.

Rule #11: Give feature columns owners and documentation.

Rule #12: Don’t overthink which objective you choose to directly optimize.

Rule #13: Choose a simple, observable and attributable metric for your first objective.

Rule #14: Starting with an interpretable model makes debugging easier.

Rule #15: Separate Spam Filtering and Quality Ranking in a Policy Layer.

Rule #16: Plan to launch and iterate.

Rule #17: Start with directly observed and reported features as opposed to learned features.

Rule #18: Explore with features of content that generalize across contexts.

Rule #19: Use very specific features when you can.

Rule #20: Combine and modify existing features to create new features in human¬-understandable ways.

Rule #21: The number of feature weights you can learn in a linear model is roughly proportional to the amount of data you have.

Rule #22: Clean up features you are no longer using.

Rule #23: You are not a typical end user.

Rule #24: Measure the delta between models.

Rule #25: When choosing models, utilitarian performance trumps predictive power.

Rule #26: Look for patterns in the measured errors, and create new features.

Rule #27: Try to quantify observed undesirable behavior.

Rule #28: Be aware that identical short-term behavior does not imply identical long-term behavior.

Rule #29: The best way to make sure that you train like you serve is to save the set of features used at serving time, and then pipe those features to a log to use them at training time.

Rule #30: Importance-weight sampled data, don’t arbitrarily drop it!
Rule #31: Beware that if you join data from a table at training and serving time, the data in the table may change.

Rule #32: Re-use code between your training pipeline and your serving pipeline whenever possible.

Rule #33: If you produce a model based on the data until January 5th, test the model on the data from January 6th and after.

Rule #34: In binary classification for filtering (such as spam detection or determining interesting emails), make small short-term sacrifices in performance for very clean data.

Rule #35: Beware of the inherent skew in ranking problems.

Rule #36: Avoid feedback loops with positional features.

Rule #37: Measure Training/Serving Skew.

Rule #38: Don’t waste time on new features if unaligned objectives have become the issue.

Rule #39: Launch decisions are a proxy for long-term product goals.

Rule #40: Keep ensembles simple.

Rule #41: When performance plateaus, look for qualitatively new sources of information to add rather than refining existing signals.

Rule #42: Don’t expect diversity, personalization, or relevance to be as correlated with popularity as you think they are.

Rule #43: Your friends tend to be the same across different products. Your interests tend not to be.