ML Pipeline: Structuring Data Analysis Projects
summary
Practical guidance for structuring machine-learning/data‑analysis projects by adopting software-engineering best practices: single repository layout, code quality checks, version control, testing, and experiment tracking. Covers tools and approaches for handling large datasets, multiple feature pipelines, many models and hyperparameters—showing how to automate hyperparameter search, version data and pipelines, track experiments across machines, and achieve reproducible results.
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chapters (6)
- 0:00Introduction
- 11:25ML project problems
- 22:08Tools for solutions
- 26:42Workflow and tool integration
- 44:35Implementation and integration
- 46:48Q&A
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