Docker is a common tool for Python developers creating and deploying applications, but what do you need to know if you want to use Docker for data science and machine learning? What are the best practices if you want to start using containers for your scientific projects? This week we have Tania Allard on the show. She is a Sr. Developer Advocate at Microsoft focusing on Machine Learning, scientific computing, research and open source.

Tania has created a talk for the PyCon US 2020 which is now online. The talk is titled “Docker and Python: Making them Play Nicely and Securely for Data Science and ML.” Her talk draws on her expertise in the improvement of processes, reproducibility and transparency in research and data science. We discuss a variety of tools for making your containers more secure and results reproducible.

Tania is passionate about mentoring, open-source, and its community. She is an organizer for Mentored Sprints for Diverse Beginners, and she talks about the upcoming online sprints for PyCon US 2020. We also discuss her plans to start a podcast.

Topics:

00:00:00 – Introduction
00:01:43 – Microsoft Senior Developer Advocate Role
00:04:07 – PyCon 2020 Talk - Docker and Python: making them play nicely
00:05:34 – What is Docker?
00:10:08 – Reproducibility of project results
00:12:03 – What are the challenges of using Docker for machine learning?
00:15:06 – Getting started suggestions
00:16:26 – What metadata should be included?
00:17:48 – Creating images through stages
00:21:16 – What about your data?
00:22:40 – Kubernetes: Orchestrating containers
00:24:37 – Continuing stages into testing
00:25:37 – What are tools for testing security?
00:27:07 – Challenges in using containers for ML
00:28:52 – What types of databases?
00:29:39 – Are you doing initial research on a local machine?
00:30:59 – An example of a recent ML project
00:32:16 – Papermill: parameterizing and executing notebooks
00:33:16 – NLP: Natural Language Processing
00:33:58 – Kaggle: Help us better understand COVID-19
00:34:42 – What are other best practices for data intensive projects?
00:39:13 – Resources to get started in machine learning?
00:40:30 – Mentored Sprints for Diverse Beginners
00:45:34 – Tania’s upcoming podcast
00:48:38 – A visiting fellow at the Alan Turing Institute
00:49:08 – Weight lifting
00:50:16 – Craft beer
00:52:09 – What is something you thought you knew in Python but were wrong about?
00:53:50 – What are excited about in the world of Python?
00:54:42 – Thank you and Goodbye

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