Docker for Data Science: Reproducibility and Deployment
summary
Live demos show concrete wins and gotchas: reproducible environments replace juggling six virtualenvs, an docker run hello-world and docker build -t dev illustrate images and cached layers, and a dockerized pipeline converts legacy .XLS via ssconvert so one container handles messy files. Examples include a Postgres + PostGIS base image, serving a Jupyter notebook from a container with mounted volumes, and deploying a pickled scikit-learn model behind a Flask prediction endpoint that returns live predictions.
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chapters (11)
- 0:00Introduction
- 2:56Why Docker matters
- 6:11First steps: Hello World
- 8:09Running Ubuntu container
- 15:56Images vs containers
- 20:09Custom images with Dockerfiles
- 26:49Virtual env chaos solved
- 28:59Jupyter in Docker
- 41:22ML model API deployment
- 54:54Advanced scaling
- 1:00:05Q&A
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