Data Management for Machine Learning

14 lectures

How data is stored, cleaned, versioned, and served to power reliable machine-learning pipelines.

01Lecture 01 · Data Management for Machine Learning: Foundations and Data Representation

02Lecture 02 · Query Paradigms, Storage Architectures, and Data Pipelines

03Lecture 03 · Data Pre-Processing, Data Architecture, and Warehouse Schemas

04Lecture 04 · Data Pipelines, Big Data Systems, and Feature Engineering

05Lecture 05 · The Modern Data Stack and Data Pipelines

06Lecture 06 · Data Pipelines, Outliers, and the Machine Learning Lifecycle

07Lecture 07 · The Machine Learning Lifecycle: From Business Understanding to Model Serving

08Lecture 08 · Data Ingestion, Validation, and Data Quality

09Lecture 09 · Data Integration and Data Transformation

10Lecture 10 · Orchestration, Automation, and Version Control for Data Pipelines

11Lecture 11 · Machine Learning Experimentation and Metadata

12Lecture 12 · Big Data Systems: Distributed Storage and Distributed Processing

13Lecture 13 · Big Data Ecosystems, Cloud Platforms, and LLM Pipelines

14Lecture 14 · Data Privacy, Governance and LLM Data Pipelines

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