Making Machine Learning Reproducible

Reproducibility efforts are community efforts, as this episode's guest Grigori Fursin makes very clear. But you also need the tools. 
For some time, Grigori worked on the Collective Knowledge (CK) Framework to help researchers and machine learning practitioners get the best out of their solutions. 
In this episode we talk about the challenges you face when trying to evaluate machine learning applications and taking them to production. And how tools like CK Framework and others can help.

• https://cknowledge.org - Collective Knowledge (CK) Framework web site 

• https://mlcommons.org/en/ - ML Commons, a non-profit organisation & community for tools around machine learning applications: in particular ML Perf for performance testing

• https://github.com/mlcommons/ck  - CK framework GitHub repository

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