MLflow Training Course

Introduction

• 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 deployment, 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 on 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 reusability.
• 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 with Apache Spark -- optional.

Troubleshooting

Summary and Conclusion