"Docker containers are a popular way to create reproducible development environments without having to install complex dependencies on your local machine. Developers all over the world use them for production and R&D environments.
However, using Docker for Machine Learning is not always straightforward. Plus most of the tutorials and content out there focus on how to use Docker to containerize apps rather than focusing on Data Science solutions.
In this talk, Tania shares some tips and tricks on how to effectively use Docker for Machine Learning and Data Science, helping to make your work more robust and reproducible."