MLflow: Managing the Machine Learning Lifecycle

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.

Update: this course has been updated with the new Model Registry features.

Note: This course will not run on Databricks Community Edition.

Description

Length

3-6 hours, 75% hands-on

Format: Self-paced

The course is a series of five 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.

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. Model registry

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.

Duration

8 hours