resources on ml production, interpretation and observability

❤️ Want to support this project? Forward this email to three friends!

🚀 Got this forwarded from a friend? Sign up here

1. Machine Learning in production: the Booking.com approach

2. Accelerating Machine Learning and Artificial Intelligence sprints through DevOps best practices

3. Should you build your own deep learning rig? : Notes on how To Get Free GPU Hardware and what to do when you outgrow it

4. Observability for data engineering pipelines

5. fairlearn 0.4.1, the latest release of the Python package for assessing the fairness and mitigating the observed unfairness of AI systems, is now available

6. Where do you put commas in your queries?

   

7. Optimizing sample sizes in a/b testing

8. How to use airflow without headaches / Similar here: Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

9. Software Engineering for AI/ML -- An Annotated Bibliography

10. GitHub now helps you find good first issues to start contributing to open source

11. Google Research: Looking Back at 2019, and Forward to 2020 and Beyond

12. A Bayesian machine scientist to aid in the solution of challenging scientific problems

13. Bouncing it again: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

14. Jeremy Howard’s data project checklist

15. Podcast: Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards

16. No easy getting laid: Tinder is using machine learning to identify potential toxic messages

17. ML-fairness-gym: A Tool for Exploring Long-Term Impacts of Machine Learning Systems

18. Notes on machine learning systems design

19. A billion rows a second - working with BIG data in Python

20. Uber Open Sources Manifold, a Visual Debugging Tool for Machine Learning