Streamlining AI Prototyping and Deployment with R and MLflow
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Streamlining AI Prototyping and Deployment with R and MLflow
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We provide a recap of the MLflow R interface which was announced at Spark+AI Summit Europe and discuss recent developments. The session includes a live demo showcasing the intersection of big data (Spark) and deep learning (via TensorFlow) and how the end-to-end lifecycle from prototyping to deployment can be managed by MLflow.
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Streamlining AI Prototyping and Deployment with R and MLflow
- 1. Kevin Kuo @kevinykuo, RStudio Streamlining AI Prototyping and Deployment with R and MLflow #UnifiedAnalytics #SparkAISummit
- 2. Daily specials - Quick update on the R ecosystems for AI stuff - Recap of MLflow - Demo - Discussion + Q&A 2#UnifiedAnalytics #SparkAISummit
- 3. Sparklyr Update - Arrow integration to massively speed up UDFs - XGBoost - TFRecord read/write - SparkNLP on the way https://spark.rstudio.com/ 3#UnifiedAnalytics #SparkAISummit
- 6. TensorFlow Update He looks skeptical, as if you were nothing get it right. It drives me crazy, I can do this reproachful look no longer endure. His name is Olaf. 6#UnifiedAnalytics #SparkAISummit
- 7. TensorFlow Update - library(keras) defaults to tf.keras - TensorFlow Probability for probabilistic modeling - Eager execution - Preparing for TF2.0 drop https://tensorflow.rstudio.com/ https://blogs.rstudio.com/tensorflow/ 7#UnifiedAnalytics #SparkAISummit
- 8. Quick recap of MLflow Open source platform for - Experiment instrumentation (Tracking) - Reproducible runs (Projects) - Model deployment (Models) 8#UnifiedAnalytics #SparkAISummit
- 9. Tracking Keeping track of stuff mlflow_log_param("num_hidden_units", 64) mlflow_log_artifact("training_history.png") mlflow_log_metric("accuracy", metrics$acc) 9#UnifiedAnalytics #SparkAISummit
- 10. Projects Packaging up (reproducible) building blocks mlflow_run("data-prep.R") 10#UnifiedAnalytics #SparkAISummit
- 11. Models Deployment flavors: keras: version: 2.2.2 data: model.h5 python_function: loader_module: mlflow.keras data: model.h5 env: conda_env.yaml utc_time_created: 19-04-25T01:00:21.21.72 11#UnifiedAnalytics #SparkAISummit mlflow_rfunc_serve( "keras_model", run_uuid = training_run_id )
- 13. Roadmap How are package dependencies handled for R projects? Conda? Packrat? What if your packages depend on Java/Python libraries? 13#UnifiedAnalytics #SparkAISummit
- 14. Quick excursion on dependency management 14#UnifiedAnalytics #SparkAISummit
- 15. Renv is in 15#UnifiedAnalytics #SparkAISummit renv::init() renv::restore()
- 16. Renv is in 16#UnifiedAnalytics #SparkAISummit Conda support in progress for reticulated packages
- 17. What about... What about stuff with Java/rJava dependencies?!?! 17#UnifiedAnalytics #SparkAISummit
- 18. Betting on new tech Why MLflow and not something else? 18#UnifiedAnalytics #SparkAISummit
- 19. Roadmap Better integration with deployment tech - MLeap (https://github.com/rstudio/mleap) - H2O - TensorFlow Serving - Arbitrary R models (plumber + docker) 19#UnifiedAnalytics #SparkAISummit
- 20. Resources - https://mlflow.org/ - https://github.com/mlflow/mlflow - https://tensorflow.rstudio.com/ - https://spark.rstudio.com/ - https://community.rstudio.com/ - Demo Repo: https://github.com/kevinykuo/sais2019-mlflow 20#UnifiedAnalytics #SparkAISummit
- 21. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT