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Git for Data Science
Git is becoming a crucial tool for data scientists as teams work on larger, more production-focused ML projects. So Juha put together six rules of thumb to avoid the biggest pitfalls with Git.
Docker for Data Science
Docker is the next layer of engineering practices in our series. Juha Kiili compares Docker to a spacesuit. It keeps your code insulated from the elements and the environment inside is always the same.
IDEs for Data Science
Programming in a vanilla notebook might be fine for small things, but engineering for production without proper tooling is not recommended, and the gap is widening every day in the wake of new AI-assisted programming tools.
Dependency Management for Data Science
In this blog post Juha continues exploring engineering for data scientists with Dependency Management. So if you are curious about virtual environments and Python dependencies, this is an excellent place to start.