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Google's Machine Learning Crash Course - 43 Rules of Machine Learning (summary)

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.

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