MLflowのトレーニングコース

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 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

Troubleshooting

Summary and Conclusion