メインコンテンツにスキップ

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

無料アカウントを作成する

練習とトラックであなたの進捗を記録しましょう。

Googleで登録続行すると、次に同意したものとみなされます： 利用規約、 プライバシーポリシー 、およびデータが米国内に保管されることに同意したものとみなされます。

一緒にコーディング：

コードアロング ↗

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

トピック

Data EngineeringMachine LearningPythonData Scientist

関連

チュートリアル

Evaluating LLMs with MLflow: A Practical Beginner’s Guide

もっと見る →