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 testinghttps://github.com/mlcommons/ck  - CK framework GitHub repositoryGet in touchThank you for listening! Merci de votre écoute! Vielen Dank für´s Zuhören!Contact Details/ Coordonnées / Kontakt:Email mailto:[email protected] RSE Slack (ukrse.slack.com): @code4thought or @piddie Bluesky: https://bsky.app/profile/code4thought.bsky.socialLinkedIn: https://www.linkedin.com/in/pweschmidt/  (personal Profile)LinkedIn: https://www.linkedin.com/company/codeforthought/ (Code for Thought Profile)This podcast is licensed under the Creative Commons Licence:  https://creativecommons.org/licenses/by-sa/4.0/