Keys to Building Machine Learning Systems
summary
Enterprises report 15% doing anything with ML while 85% are only evaluating; adoption stalls from process, not data. The prescription is concrete: embed data scientists in engineering teams or unify reporting, build automate end-to-end on day one pipelines that version experiments, run CI/CD for models, and measure success with explicit metrics. Expect high churn — 70–80% of code thrown away — so plan for ongoing refactoring, tests, experiment tracking (Guild AI, TensorFlow/PyTorch workflows) and incremental releases.
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chapters (13)
- 0:00Intro
- 1:21What is machine learning?
- 2:04AI adoption in the enterprise 2020
- 5:30The fundamental problem in most organizations
- 6:07Data science vs. traditional software
- 8:58ML systems development
- 14:14Unify data science & engineering teams
- 15:43Use software best practices
- 16:57Identify model success criteria metrics
- 19:20Solve a simple problem first
- 20:10Automate end-to-end on day 1
- 22:22Ongoing refactoring with tests
- 25:05Keys to building ML systems (summary)
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