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Introducing MLflow for R

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We introduce the R API for MLflow, which is an open source platform for managing the machine learning lifecycle. We demonstrate each component of the platform–Tracking, Projects, and Models–and describe how they can be leveraged in practical data science workflows.

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Introducing MLflow for R

  1. 1. Introducing MLflow for R Managing the Machine Learning Lifecycle Kevin Kuo January 2019
  2. 2. 2 / 20
  3. 3. Motivation 3 / 20
  4. 4. Keeping track of what you did 4 / 20
  5. 5. 5 / 20
  6. 6. Replicating results 6 / 20
  7. 7. 7 / 20
  8. 8. Bridging the gap 8 / 20
  9. 9. Data scientist vs. ML engineer 9 / 20
  10. 10. Data scientist vs. ML engineer 10 / 20
  11. 11. XGBoost, TensorFlow, random CRAN packages 11 / 20
  12. 12. These problems had already been solved... 12 / 20
  13. 13. These problems had already been solved... for some. 13 / 20
  14. 14. eng.uber.com code.fb.com 14 / 20
  15. 15. But what if you're not a big tech company? 15 / 20
  16. 16. PMML? PFA? MLflow? New vendor in the exhibit hall? 16 / 20
  17. 17. Efforts in the R ecosystem (excerpt) mleap: MLeap integration for sparklyr for serializing Spark ML pipelines tfruns: Track and Visualize Training Runs (for TF and Keras) packrat: Dependency management system for R. RStudio Connect: Native TF model deployment, arbitrary R models via plumber RStudio Connect: Reproducible report publishing and sharing mlflow: interface to MLflow 17 / 20
  18. 18. Efforts in the R ecosystem (excerpt) mleap: MLeap integration for sparklyr for serializing Spark ML pipelines tfruns: Track and Visualize Training Runs (for TF and Keras) packrat: Dependency management system for R. RStudio Connect: Native TF model deployment, arbitrary R models via plumber RStudio Connect: Reproducible report publishing and sharing mlflow: interface to MLflow We likely won't ever solve everyone's problems with one framework, but we should be able to standardise on 90% of the problems and have good/generally accepted guidance on the rest. 18 / 20
  19. 19. MLflow Tracking: keep track of your parameters, notes, and metrics for experiments. Project: bundle your project and environment so others can reproduce your results. Model: serialize and package your scoring function for serving locally and on the cloud. 19 / 20
  20. 20. MLflow Tracking: keep track of your parameters, notes, and metrics for experiments. Project: bundle your project and environment so others can reproduce your results. Model: serialize and package your scoring function for serving locally and on the cloud. DEMO! 20 / 20

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