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Introducing MLflow for
End-to-End Machine Learning
on Databricks
Sean Owen
Principal Solutions Architect @ Databricks
A Data Science Lifecycle in Many Steps
▪ Enable access to data
▪ Secure data
▪ Clean and standardize
data
▪ Make it discov...
Data Science and ML on
AutoML
End-to-End ML Lifecycle
ML Runtime and
Environments
Batch
Scoring
Online
Serving
Data Scienc...
Demo:
Predicting Life Expectancy
Updated!
Feedback
Your feedback is important to us.
Don’t forget to rate
and review the sessions.
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Introducing MLflow for End-to-End Machine Learning on Databricks Slide 1 Introducing MLflow for End-to-End Machine Learning on Databricks Slide 2 Introducing MLflow for End-to-End Machine Learning on Databricks Slide 3 Introducing MLflow for End-to-End Machine Learning on Databricks Slide 4 Introducing MLflow for End-to-End Machine Learning on Databricks Slide 5
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Introducing MLflow for End-to-End Machine Learning on Databricks

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Solving a data science problem is about more than making a model. It entails data cleaning, exploration, modeling and tuning, production deployment, and workflows governing each of these steps. In this simple example, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and autologging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or REST endpoint. This tutorial will cover the latest innovations from MLflow 1.12.

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Introducing MLflow for End-to-End Machine Learning on Databricks

  1. 1. Introducing MLflow for End-to-End Machine Learning on Databricks Sean Owen Principal Solutions Architect @ Databricks
  2. 2. A Data Science Lifecycle in Many Steps ▪ Enable access to data ▪ Secure data ▪ Clean and standardize data ▪ Make it discoverable ▪ Make it reliable ▪ Join and aggregate relevant data ▪ Explore and visualize it ▪ Experiment with modeling techniques ▪ Tune and select a best model ▪ Translate model to production-ready form ▪ Execute inference ▪ Monitor modeling jobs ▪ Operate data pipelines Data Science ProductionizationData Engineering
  3. 3. Data Science and ML on AutoML End-to-End ML Lifecycle ML Runtime and Environments Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture
  4. 4. Demo: Predicting Life Expectancy Updated!
  5. 5. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.
  • priancho

    Jan. 6, 2021

Solving a data science problem is about more than making a model. It entails data cleaning, exploration, modeling and tuning, production deployment, and workflows governing each of these steps. In this simple example, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and autologging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or REST endpoint. This tutorial will cover the latest innovations from MLflow 1.12.

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