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.
涵盖的内容
6个视频3篇阅读材料4个作业
显示有关单元内容的信息
6个视频•总计27分钟
Why ETL and ELT Matter for ML Pipelines•6分钟
Orchestrating Daily Pipelines with Airflow•5分钟
Why Schema Changes Break Pipelines•5分钟
Applied Walkthrough: Updating Transform Logic for Schema Changes•4分钟
From Pipeline Runs to SLAs•4分钟
Congratulations and Continuous Learning Journey•3分钟
3篇阅读材料•总计22分钟
ETL vs. ELT Patterns in Modern ML Systems•8分钟
Schema Evolution and Backward Compatibility•8分钟
Seeing the Whole Pipeline: From Ingestion to SLAs •6分钟
4个作业•总计95分钟
Hands-On Activity: Design a Daily Airflow DAG•20分钟
Hands-On Activity: Interpreting Pipeline Metrics and Detecting SLA Breaches •15分钟
Hands-On Activity: End-to-End ML of a Pipeline Reliability Lab•40分钟
Graded Quiz: Evaluating ML Pipeline Design and Reliability•20分钟
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