Podcast: Code for Thought
Erschienen: 23.11.2021
Dauer: 00:30:39
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 repositorySupport the showThank you for listening and your ongoing support. It means the world to us! You can also support our efforts by leaving a rating or review.Follow or contact us on Email mailto:code4thought@proton.me Patreon https://www.patreon.com/codeforthought Slack (ukrse.slack.com): @code4thought Mastadon: @code4thought@fosstodon.org LinkedIn: https://www.linkedin.com/in/pweschmidt/ This podcast is licensed under the Creative Commons Licence: https://creativecommons.org/licenses/by-sa/4.0/
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Podcast-Website: Episode "Making Machine Learning Reproducible"
