MLflow Training Course

Introduction

• Adapting software development best practices for machine learning.
• MLflow vs Kubeflow — where does MLflow excel?

Overview of the Machine Learning Lifecycle

• Data preparation, model training, model deployment, model serving, and more.

Overview of MLflow Features and Architecture

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

Setting up MLflow

• Installation in a public cloud environment
• Installation on an on-premise server

Preparing the Development Environment

• Working with Jupyter notebooks, Python IDEs, and standalone scripts

Preparing a Project

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

Using MLflow Tracking

• Logging code versions, data, and configurations
• Logging output files and performance 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

• Selecting a deployment environment (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
• Collaborative model management

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