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In diesem Kurs gibt es 1 Modul
AI projects succeed or fail on the strength of their data. In this course, you’ll learn to diagnose pipeline bottlenecks, enforce governance standards, and plan data workflows that keep projects on track. Through real-world scenarios, you’ll trace issues like stale feature vectors, audit data handling for compliance with regulations such as General Data Protection Regulation (GDPR), and design actionable plans for data acquisition, storage, and annotation. You’ll also practice translating technical findings into project-ready deliverables, like drafting a Jira epic for large-scale data labeling.
AI projects succeed or fail on the strength of their data. In this course, you’ll learn to diagnose pipeline bottlenecks, enforce governance standards, and plan data workflows that keep projects on track. Through real-world scenarios, you’ll trace issues like stale feature vectors, audit data handling for compliance with regulations such as General Data Protection Regulation (GDPR), and design actionable plans for data acquisition, storage, and annotation. You’ll also practice translating technical findings into project-ready deliverables, like drafting a Jira epic for large-scale data labeling.
Das ist alles enthalten
6 Videos3 Lektüren4 Aufgaben
Infos zu Modulinhalt anzeigen
6 Videos•Insgesamt 21 Minuten
Introduction and Welcome•3 Minuten
Tracing a Pipeline Bottleneck: Stale Features in Spark•3 Minuten
Would Your AI Pass an Audit•3 Minuten
Auditing S3 Permissions for Compliance•4 Minuten
Planning Annotation Work•7 Minuten
Congratulations and Continuous Learning Journey •2 Minuten
3 Lektüren•Insgesamt 32 Minuten
Why Stale Data Is a Silent Killer in AI/ML Pipelines•10 Minuten
Responsible AI Training Guide •12 Minuten
Creating a Comprehensive Data Plan for AI Projects•10 Minuten
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