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.

1. Introducing MLflow for R
Managing the Machine Learning Lifecycle
Kevin Kuo
January 2019

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3. Motivation
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4. Keeping track of what you did
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5. 5 / 20

6. Replicating results
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7. 7 / 20

8. Bridging the gap
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9. Data scientist vs. ML engineer
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10. Data scientist vs. ML engineer
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11. XGBoost, TensorFlow, random CRAN packages
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12. These problems had already been solved...
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13. These problems had already been solved...
for some.
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14. eng.uber.com code.fb.com 14 / 20

15. But what if you're not a big tech company?
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16. PMML? PFA? MLflow? New vendor in the exhibit hall?
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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
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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.
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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.
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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!
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