Перейти к основному содержимому

Managing Machine Learning Models with MLflow

Learn to use MLflow to track and package a machine learning model, and see the process for getting models into production.
Weston Bassler
Weston Bassler

Создать бесплатный аккаунт

Практикуйтесь и следите за своим прогрессом.

Продолжить с GoogleПродолжая, вы принимаете нашу Условия использования, наши Политику конфиденциальности и то, что ваши данные хранятся в США.

Пишите код вместе с нами на

Code Along ↗

Machine Learning applications are complex and can be difficult to track, hard to reproduce, and problematic to deploy. MLflow is designed to simplify the challenges of managing the machine learning lifecycle.

In this code-along you'll learn to use MLflow to track and package a machine learning model, and see the process for getting models into production. Throughout the code-along, you’ll learn how to get started with MLflow, how to make a reproducible machine learning model, and how to get started with model tracking and packaging.

Key Takeaways:

  • Learn how to get started with MLflow.
  • Learn the steps needed to make a reproducible model.
  • Learn about model tracking and packaging.

Resources

Темы

Data EngineeringMachine LearningPythonData Scientist

Связанное

Учебное руководство

Evaluating LLMs with MLflow: A Practical Beginner’s Guide

Показать больше →