MLflow Training Course

Introduction

• Adapting software development best practices to machine learning for government applications.
• Evaluating MLflow versus Kubeflow: where does MLflow excel in the context of government projects?

Overview of the Machine Learning Cycle

• Data preparation, model training, model deployment, and model serving are key components of the machine learning lifecycle for government.

Overview of MLflow Features and Architecture

• MLflow Tracking, MLflow Projects, and MLflow Models: essential tools for managing the machine learning workflow in a government setting.
• Utilizing the MLflow command-line interface (CLI) to streamline operations.
• Navigating the MLflow user interface to monitor and manage projects effectively.

Setting up MLflow for Government

• Installing MLflow in a public cloud environment to leverage scalable resources.
• Deploying MLflow on an on-premise server to ensure data security and compliance with government regulations.

Preparing the Development Environment

• Utilizing Jupyter notebooks, Python integrated development environments (IDEs), and standalone scripts for efficient development.

Preparing a Project

• Establishing secure connections to data sources for government projects.
• Creating prediction models tailored to specific government needs.
• Training models using robust datasets and algorithms.

Using MLflow Tracking

• Logging code versions, data sets, and configuration parameters to ensure reproducibility.
• Tracking output files and performance metrics for comprehensive project documentation.
• Querying and comparing results to optimize model performance.

Running MLflow Projects

• Understanding the YAML syntax for defining project configurations.
• Leveraging Git repositories to manage version control and collaboration.
• Packaging code for reusability in government projects.
• Fostering collaboration by sharing code and working with team members efficiently.

Saving and Serving Models with MLflow Models

• Selecting the appropriate deployment environment (cloud, standalone application) based on project requirements.
• Deploying machine learning models to production environments securely.
• Serving models to end-users through scalable and reliable infrastructure.

Using the MLflow Model Registry for Government

• Setting up a centralized repository for model management in government agencies.
• Storing, annotating, and discovering models to facilitate knowledge sharing.
• Collaboratively managing models to ensure consistency and compliance with regulatory standards.

Integrating MLflow with Other Systems for Government

• Utilizing MLflow plugins to enhance functionality.
• Integrating with third-party storage systems, authentication providers, and REST APIs to support comprehensive data management.
• Optionally integrating Apache Spark for large-scale data processing in government projects.

Troubleshooting

• Identifying and resolving common issues to ensure smooth operation of MLflow in government environments.

Summary and Conclusion