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Automate ML Pipelines for Peak Performance

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Automate ML Pipelines for Peak Performance

This course is part of multiple programs.

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Intermediate level

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2 hours to complete
Flexible schedule
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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

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Recently updated!

March 2026

Assessments

2 assignments¹

AI Graded see disclaimer
Taught in English

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There is 1 module in this course

This course teaches you how to build a fully automated machine learning pipeline using scikit-learn. You will learn to scale numeric features, encode categorical variables, train a logistic model, and optimize it using GridSearchCV. The course then guides you in packaging the workflow as a reusable module that fits real-world ML engineering and MLOps practices. Through concise videos, structured readings, two 15-minute Coach interactions, a combined 25-minute hands-on activity, and a 45-minute ungraded lab, you will practice constructing and refining an end-to-end pipeline. By the end, you will have a polished, automated workflow you can reuse, adapt, and integrate into your ML projects or production systems.

This course teaches you how to build a fully automated machine learning pipeline using scikit-learn. You will learn to scale numeric features, encode categorical variables, train a logistic model, and optimize it using GridSearchCV. The course then guides you in packaging the workflow as a reusable module that fits real-world ML engineering and MLOps practices. Through concise videos, structured readings, two 15-minute Coach interactions, a combined 25-minute hands-on activity, and a 45-minute ungraded lab, you will practice constructing and refining an end-to-end pipeline. By the end, you will have a polished, automated workflow you can reuse, adapt, and integrate into your ML projects or production systems.

What's included

4 videos2 readings2 assignments1 ungraded lab

4 videos•Total 33 minutes
  • Why Automation Improves ML Performance•4 minutes
  • Pipeline Fundamentals: Scaling, Encoding, and Workflow Structure•15 minutes
  • Automating Model Optimization with GridSearchCV•12 minutes
  • Congratulations and Continuous Learning Journey•3 minutes
2 readings•Total 20 minutes
  • Building a Strong Foundation: Preprocessing, Logistic Regression, and Workflow Setup•10 minutes
  • Publishing Pipelines as Reusable Modules: A Practical Guide•10 minutes
2 assignments•Total 45 minutes
  • Graded Quiz: Automate ML Pipelines for Peak Performance•20 minutes
  • Hands-On Activity: Build, Tune, and Finalize Your Automated Pipeline•25 minutes
1 ungraded lab•Total 45 minutes
  • Build and Publish a Complete Automated Pipeline Module•45 minutes

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.