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URL: https://www.coursera.org/learn/orchestrate-analyze-and-evaluate-ml-pipelines

⇱ Orchestrate, Analyze, and Evaluate ML Pipelines | Coursera

Orchestrate, Analyze, and Evaluate ML Pipelines

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Orchestrate, Analyze, and Evaluate ML Pipelines

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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
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

4 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Gradient to Production: MLOps & Model Serving Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There is 1 module in this course

This course teaches you how to design, evaluate, and operate reliable machine learning data pipelines in production. You’ll learn how daily ETL and ELT pipelines feed feature stores, how orchestration supports reproducible feature engineering, how to handle upstream schema changes without breaking downstream systems, and how to evaluate pipeline health using freshness, lag, and SLA metrics. Designed for data engineers, analytics engineers, and ML practitioners, the course builds job-ready judgment for delivering timely, trustworthy, and resilient data to ML systems.

This course teaches you how to design, evaluate, and operate reliable machine learning data pipelines in production. You’ll learn how daily ETL and ELT pipelines feed feature stores, how orchestration supports reproducible feature engineering, how to handle upstream schema changes without breaking downstream systems, and how to evaluate pipeline health using freshness, lag, and SLA metrics. Designed for data engineers, analytics engineers, and ML practitioners, the course builds job-ready judgment for delivering timely, trustworthy, and resilient data to ML systems.

What's included

6 videos3 readings4 assignments

6 videos•Total 27 minutes
  • Why ETL and ELT Matter for ML Pipelines•6 minutes
  • Orchestrating Daily Pipelines with Airflow•5 minutes
  • Why Schema Changes Break Pipelines•5 minutes
  • Applied Walkthrough: Updating Transform Logic for Schema Changes•4 minutes
  • From Pipeline Runs to SLAs•4 minutes
  • Congratulations and Continuous Learning Journey•3 minutes
3 readings•Total 22 minutes
  • ETL vs. ELT Patterns in Modern ML Systems•8 minutes
  • Schema Evolution and Backward Compatibility•8 minutes
  • Seeing the Whole Pipeline: From Ingestion to SLAs •6 minutes
4 assignments•Total 95 minutes
  • Graded Quiz: Evaluating ML Pipeline Design and Reliability•20 minutes
  • Hands-On Activity: Design a Daily Airflow DAG•20 minutes
  • Hands-On Activity: Interpreting Pipeline Metrics and Detecting SLA Breaches •15 minutes
  • Hands-On Activity: End-to-End ML of a Pipeline Reliability Lab•40 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.