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Slides for ML @ Netflix (Paris ML meetup talk)

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Paris ML meetup

  1. Machine Learning @ Netflix (and some lessons learned) Yves Raimond (@moustaki) Research/Engineering Manager Search & Recommendations Algorithm Engineering
  2. Netflix evolution
  3. Netflix scale ● > 69M members ● > 50 countries ● > 1000 device types ● > 3B hours/month ● 36% of peak US downstream traffic
  4. Recommendations @ Netflix ● Goal: Help members find content to watch and enjoy to maximize satisfaction and retention ● Over 80% of what people watch comes from our recommendations ● Top Picks, Because you Watched, Trending Now, Row Ordering, Evidence, Search, Search Recommendations, Personalized Genre Rows, ...
  5. ▪ Regression (Linear, logistic, elastic net) ▪ SVD and other Matrix Factorizations ▪ Factorization Machines ▪ Restricted Boltzmann Machines ▪ Deep Neural Networks ▪ Markov Models and Graph Algorithms ▪ Clustering ▪ Latent Dirichlet Allocation ▪ Gradient Boosted Decision Trees/Random Forests ▪ Gaussian Processes ▪ … Models & Algorithms
  6. Some lessons learned
  7. Build the offline experimentation framework first
  8. When tackling a new problem ● What offline metrics can we compute that capture what online improvements we’ re actually trying to achieve? ● How should the input data to that evaluation be constructed (train, validation, test)? ● How fast and easy is it to run a full cycle of offline experimentations? ○ Minimize time to first metric ● How replicable is the evaluation? How shareable are the results? ○ Provenance (see Dagobah) ○ Notebooks (see Jupyter, Zeppelin, Spark Notebook)
  9. When tackling an old problem ● Same… ○ Were the metrics designed when first running experimentation in that space still appropriate now?
  10. Think about distribution from the outermost layers
  11. 1. For each combination of hyper-parameter (e.g. grid search, random search, gaussian processes…) 2. For each subset of the training data a. Multi-core learning (e.g. HogWild) b. Distributed learning (e.g. ADMM, distributed L-BFGS, …)
  12. When to use distributed learning? ● The impact of communication overhead when building distributed ML algorithms is non-trivial ● Is your data big enough that the distribution offsets the communication overhead?
  13. Example: Uncollapsed Gibbs sampler for LDA (more details here)
  14. Design production code to be experimentation-friendly
  15. Idea Data Offline Modeling (R, Python, MATLAB, …) Iterate Implement in production system (Java, C++, …) Missing post- processing logic Performance issues Actual outputProduction environment (A/B test) Code discrepancies Final model Data discrepancies Example development process
  16. Avoid dual implementations Shared Engine Experiment code Production code ProductionExperiment
  17. To be continued...
  18. We’re hiring! Yves Raimond (@moustaki)
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Slides for ML @ Netflix (Paris ML meetup talk)

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