跳至内容
首页Python

学习路径

Machine Learning in Production in Python

更新时间 2026年3月
Elevate your machine learning skills to production level with this focused skill track.
免费开始学习路径
PythonMachine Learning19 小时4,214

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

深受数千家公司学习者的喜爱

Group

培训2人或更多？

试用DataCamp for Business

学习路径描述

Machine Learning in Production in Python

Elevate your machine learning skills to production level with this focused skill track. Designed for (aspiring) data scientists and machine learning engineers, this track offers a streamlined pathway to mastering the deployment and maintenance of machine learning models.Dive into the fundamentals of MLOps, including strategies for efficient model lifecycle management. Dive into MLflow to master interactively the deployment and tracking of machine learning models with one of the most popular MLOps tools, and apply the learned skills in a hands-on project.The track further enhances your ability to maintain and monitor deployed models, ensuring they continue to perform optimally amidst evolving data landscapes. You’ll also explore the practical benefits of versioning data using Data Version Control (DVC) to manage and version your machine learning projects effectively.By the end of this skill track, you'll be proficient in deploying, monitoring, and managing machine learning models, preparing you to successfully implement ML applications in any organization.

先决条件

此学习路径无先决条件

  • Course

    1

    MLOps Concepts

    Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

  • Course

    2

    Introduction to MLflow

    Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

  • Project

    额外

    Predicting Temperature in London

    Perform a machine learning experiment to find the best model that predicts the temperature in London!

  • Course

    4

    Monitoring Machine Learning Concepts

    Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Machine Learning in Production in Python
5 课程
学习路径完成

获得成就证明

将此证书添加到你的 LinkedIn 档案、简历或履历中
在社交媒体和绩效评估中分享立即注册

加入超过19百万学习者，今天就开始Machine Learning in Production in Python！

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

通过 DataCamp for Mobile 提升您的数据技能

随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。