SP863-Az: MLflow: Managing the Machine Learning Lifecycle on Azure Databricks

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The training is priced from $ 75.00 USD per participant



Summary

In this course data scientists and data engineers learn the best practices for managing experiments, projects, and models using MLflow. By the end of this course, you will have built a pipeline to log and deploy machine learning models using the environment they were trained with.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.

Description

Length

3-6 hours, 75% hands-on

Format: Self-paced

The course is a series of six self-paced lessons available in Python. A final capstone project involves packaging an MLflow-based workflow that includes pre-processing logic, the optimal ML algorithm and hyperparameters, and post-processing logic. Each lesson includes hands-on exercises.

Platform

This version of the course is intended to be run on Azure Databricks.

Note: Access to a Databricks workspace is not part of your course purchase price. You are responsible for getting access to Databricks. See the FAQ for instructions on how to get access to an Databricks workspace.

Learning Objectives

During this course learners

  • Track machine learning experiments to organize the machine learning life cycle
  • Create, organize, and package machine learning projects with a focus on reproducibility and collaborating with a team
  • Manage the complexity of multistep machine learning projects using multistep workflows
  • Develop a generalizable way of handling machine learning models created in and deployed to a variety of environments
  • Apply what you learned with a capstone project where you create a workflow that includes pre-processing logic, the optimal ML algorithm and hyperparameters, and post-processing logic

Lessons

  1. Course Overview and Setup
  2. Experiment Tracking
  3. Packaging ML Projects
  4. Multistep Workflows
  5. Model Management
  6. Capstone Project

Target Audience

Primary Audience: Data Scientists and Data Engineers

Prerequisites

  • Python (pandas, sklearn, numpy)
  • Background in machine learning and data science

Lab Requirements

Please be sure to use a supported browser.

License Limitations

This self-paced training course may be used by 1 user for 12 months from the date of purchase. It may not be transferred or shared with any other user.

Terms

The use of the self-paced training course is subject to the Terms of Service and the Databricks Privacy Policy.

Duration

6 hours