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In diesem Kurs gibt es 1 Modul
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.
Das ist alles enthalten
6 Videos3 LektĂĽren4 Aufgaben
Infos zu Modulinhalt anzeigen
6 Videos•Insgesamt 27 Minuten
Why ETL and ELT Matter for ML Pipelines•6 Minuten
Orchestrating Daily Pipelines with Airflow•5 Minuten
Why Schema Changes Break Pipelines•5 Minuten
Applied Walkthrough: Updating Transform Logic for Schema Changes•4 Minuten
From Pipeline Runs to SLAs•4 Minuten
Congratulations and Continuous Learning Journey•3 Minuten
3 Lektüren•Insgesamt 22 Minuten
ETL vs. ELT Patterns in Modern ML Systems•8 Minuten
Schema Evolution and Backward Compatibility•8 Minuten
Seeing the Whole Pipeline: From Ingestion to SLAs •6 Minuten
4 Aufgaben•Insgesamt 95 Minuten
Hands-On Activity: Design a Daily Airflow DAG•20 Minuten
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