Course Outline


  • Adapting software development best practices to machine learning.
  • MLflow vs Kubeflow -- where does MLflow shine?

Overview of the Machine Learning Cycle

  • Data preparation, model training, model deploying, model serving, etc.

Overview of MLflow Features and Architecture

  • MLflow Tracking, MLflow Projects, and MLflow Models
  • Using the MLflow command-line interface (CLI)
  • Navigating the MLflow UI

Setting up MLflow

  • Installing in a public cloud
  • Installing in an on-premise server

Preparing the Development Environment

  • Working with Jupyter notebooks, Python IDEs and standalone scripts

Preparing a Project

  • Connecting to the data
  • Creating a prediction model
  • Training a model

Using MLflow Tracking

  • Logging code versions, data, and configurations
  • Logging output files and metrics
  • Querying and comparing results

Running MLflow Projects

  • Overview of YAML syntax
  • The role of the Git repository
  • Packaging code for re-usability
  • Sharing code and collaborating with team members

Saving and Serving Models with MLflow Models

  • Choosing an environment for deployment (cloud, standalone application, etc.)
  • Deploying the machine learning model
  • Serving the model

Using the MLflow Model Registry

  • Setting up a central repository
  • Storing, annotating, and discovering models
  • Managing models collaboratively.

Integrating MLflow with other Systems

  • Working with MLflow Plugins
  • Integrating with third-party storage systems, authentication providers, and REST APIs
  • Working Apache Spark -- optional


Summary and Conclusion


  • Python programming experience
  • Experience with machine learning frameworks and languages


  • Data scientists
  • Machine learning engineers
  21 Hours


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