Machine Learning (ML) Engineering Practice

A Machine Learning (ML) Engineering Practice is a software engineering practice that applies to ML engineering tasks.

• Example(s):
  • a ML Engineering Best-Practice.
• See: Data Engineering Practice.

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References

2018

• ([Zinkevich, 2018]]) ⇒ Martin Zinkevich. (2018). “Rules of Machine Learning: Best Practices for ML Engineering.” In: Google's Machine Learning Crash Course
  • QUOTE:
    • 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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