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Automate, Optimize, and Monitor ML Models

This course is part of Systematic ML Optimization Specialization

Instructor: Hurix Digital

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Systematic ML Optimization Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

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.

Module details

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

What's included

1 video1 reading1 assignment1 ungraded lab

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