Automate, Optimize, and Monitor ML Models

本课程是 Systematic ML Optimization 专项课程的一部分

位教师：Hurix Digital

访问权限由 New York State Department of Labor 提供

2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Production ML systems require continuous monitoring and automated responses to maintain business value over time.
Drift detection is essential for identifying when models need retraining before performance degradation impacts business outcomes.
End-to-end automation reduces manual errors and enables scalable ML operations across multiple models and environments.
Automated tuning techniques help models improve consistently without manual trial-and-error.

您将获得的技能

要了解的详细信息

可分享的证书

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作业

4 任务¹

AI 评分请参见免责声明

授课语言：英语（English）

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本课程是 Systematic ML Optimization 专项课程专项课程的一部分

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该课程共有2个模块

Machine learning models lose accuracy over time without proper monitoring and optimization. This Short Course was created to help ML and AI professionals build robust, production-ready systems that maintain performance at scale.

By completing this course, you'll master critical MLOps skills for detecting model drift, implementing automated retraining workflows, and creating optimized ML pipelines that ensure sustained business value in production environments. By the end of this course, you will be able to: - Evaluate production model performance to detect and mitigate drift - Create an automated, end-to-end machine learning pipeline for model optimization This course is unique because it bridges the gap between model development and production operations, focusing on automation and monitoring strategies that prevent costly model failures. To be successful in this project, you should have experience with machine learning fundamentals and Python programming.

Learners will master the systematic evaluation of production ML models to identify performance degradation and implement drift detection systems that automatically trigger remediation actions.

涵盖的内容

1个视频1篇阅读材料1个作业1个非评分实验室

Learners will build comprehensive automated ML pipelines with integrated hyperparameter optimization and end-to-end automation that maintains model performance in production environments.