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Kevin Kuo @kevinykuo, RStudio
Streamlining AI
Prototyping and
Deployment with R and
MLflow
#UnifiedAnalytics #SparkAISummit
Daily specials
- Quick update on the R ecosystems for AI stuff
- Recap of MLflow
- Demo
- Discussion + Q&A
2#UnifiedAnalyt...
Sparklyr Update
- Arrow integration to massively speed up UDFs
- XGBoost
- TFRecord read/write
- SparkNLP on the way
https...
TensorFlow Update
4#UnifiedAnalytics #SparkAISummit
TensorFlow Update
5#UnifiedAnalytics #SparkAISummit
TensorFlow Update
He looks skeptical, as if you
were nothing
get it right.
It drives me crazy, I can do
this
reproachful l...
TensorFlow Update
- library(keras) defaults to tf.keras
- TensorFlow Probability for probabilistic modeling
- Eager execut...
Quick recap of MLflow
Open source platform for
- Experiment instrumentation (Tracking)
- Reproducible runs (Projects)
- Mo...
Tracking
Keeping track of stuff
mlflow_log_param("num_hidden_units", 64)
mlflow_log_artifact("training_history.png")
mlflo...
Projects
Packaging up (reproducible) building blocks
mlflow_run("data-prep.R")
10#UnifiedAnalytics #SparkAISummit
Models
Deployment
flavors:
keras:
version: 2.2.2
data: model.h5
python_function:
loader_module: mlflow.keras
data: model.h...
Demo!
12#UnifiedAnalytics #SparkAISummit
Roadmap
How are package dependencies handled for R
projects?
Conda? Packrat?
What if your packages depend on Java/Python
l...
Quick excursion on
dependency management
14#UnifiedAnalytics #SparkAISummit
Renv is in
15#UnifiedAnalytics #SparkAISummit
renv::init()
renv::restore()
Renv is in
16#UnifiedAnalytics #SparkAISummit
Conda support in
progress for reticulated
packages
What about...
What about stuff with
Java/rJava
dependencies?!?!
17#UnifiedAnalytics #SparkAISummit
Betting on new tech
Why MLflow and not
something else?
18#UnifiedAnalytics #SparkAISummit
Roadmap
Better integration with deployment tech
- MLeap (https://github.com/rstudio/mleap)
- H2O
- TensorFlow Serving
- Ar...
Resources
- https://mlflow.org/
- https://github.com/mlflow/mlflow
- https://tensorflow.rstudio.com/
- https://spark.rstud...
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT
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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. 1. Kevin Kuo @kevinykuo, RStudio Streamlining AI Prototyping and Deployment with R and MLflow #UnifiedAnalytics #SparkAISummit
  2. 2. Daily specials - Quick update on the R ecosystems for AI stuff - Recap of MLflow - Demo - Discussion + Q&A 2#UnifiedAnalytics #SparkAISummit
  3. 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
  4. 4. TensorFlow Update 4#UnifiedAnalytics #SparkAISummit
  5. 5. TensorFlow Update 5#UnifiedAnalytics #SparkAISummit
  6. 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. 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. 8. Quick recap of MLflow Open source platform for - Experiment instrumentation (Tracking) - Reproducible runs (Projects) - Model deployment (Models) 8#UnifiedAnalytics #SparkAISummit
  9. 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. 10. Projects Packaging up (reproducible) building blocks mlflow_run("data-prep.R") 10#UnifiedAnalytics #SparkAISummit
  11. 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 )
  12. 12. Demo! 12#UnifiedAnalytics #SparkAISummit
  13. 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. 14. Quick excursion on dependency management 14#UnifiedAnalytics #SparkAISummit
  15. 15. Renv is in 15#UnifiedAnalytics #SparkAISummit renv::init() renv::restore()
  16. 16. Renv is in 16#UnifiedAnalytics #SparkAISummit Conda support in progress for reticulated packages
  17. 17. What about... What about stuff with Java/rJava dependencies?!?! 17#UnifiedAnalytics #SparkAISummit
  18. 18. Betting on new tech Why MLflow and not something else? 18#UnifiedAnalytics #SparkAISummit
  19. 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. 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. 21. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

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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