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Course Outline
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
Requirements
- Python programming experience
- Experience with machine learning frameworks and languages
Audience
- Data scientists
- Machine learning engineers
21 Hours
