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Are these the best practices for machine learning operations

Recorded on the 13th of March, noontime, this engaging and enlightening talk revolves around machine learning and the best practices in machine learning engineering.
(Last Updated On: March 13, 2024)

Welcome to another exciting episode with Brian from quantlabs.net. Recorded on the 13th of March, noontime, this engaging and enlightening talk revolves around machine learning and the best practices in machine learning engineering. Although Brian admits to not being an expert, he invites listeners, even those who may not find the subject generally useful, to engage with him as he explores this intriguing world.

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The podcast delves into an article originally discovered on Reddit, within the toting subreddit. Another piece of content that sparks discussion is an article from Medium.com penned by Luis Bermondes which gives an overview of ML Ops (Machine Learning Operations). Of particular interest is a diagram depicting the ML op stack and the direction it operates in.

Brian undertakes a comprehensive walkthrough of the ML Ops stack, pointing out key areas such as the Data Collection, Experimentation, Evaluation, and Maintenance. He additionally highlights the right-hand side of the diagram, ascending from Infrastructure layer, Component layer, Pipeline layer, to Run layer.

This episode invites listeners to join the conversation about machine learning and artificial intelligence by sharing their insights and comments through various platforms. Brian encourages feedback and insights via his discord community, email, his website, or social media. Everyone is urged to share their thoughts whether they consider themselves ‘novices’ or experts in the field, contributing to this fascinating exploration.

 

medium.com/machinevision/overview-of-mlops-a07053fc2a80

reddit.com/r/coding/comments/1bd4w76/what_are_best_practices_for_machine_learning/

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