MLX: Opinionated ML Pipelines in MLflow // Xiangrui Meng // Coffee Sessions #112 - podcast episode cover

MLX: Opinionated ML Pipelines in MLflow // Xiangrui Meng // Coffee Sessions #112

Aug 03, 202250 minSeason 1Ep. 112
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Episode description

MLOps Coffee Sessions #112 with Xiangrui Meng, Principal Software Engineer of Databricks, MLX: Opinionated ML Pipelines in MLflow, co-hosted by Vishnu Rachakonda.


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// Abstract
MLX is to enable data scientists to stay mostly within their comfort zone, utilizing their expert knowledge while following the best practices in ML development and delivering production-ready ML projects, with little help from production engineers and DevOps.

// Bio
Xiangrui Meng is a Principal Software Engineer at Databricks and an Apache Spark PMC member. His main interests center around simplifying the end-to-end user experience of building machine learning applications, from algorithms to platforms and to operations.

// MLOps Jobs board

jobs.mlops.community  
MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Good Strategy Bad Strategy: The Difference and Why It Matters, book by Richard Rumelt:
https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Xiangrui on LinkedIn: https://www.linkedin.com/in/mengxr/

Timestamps:
[00:00] Introduction to Xiangrui Meng
[00:39] Takeaways
[02:09] Xiangrui's background
[03:38] What kept Xiangrui in Databricks
[07:33] What needs to be done to get there
[09:20] Machine Learning passion of Xiangrui
[11:52] Changes in building that keep you fresh for the future
[14:35] Evolution core challenges to real-time and use cases in real-time
[17:33] DevOps + DataOps + ModelOps = MLOps
[19:21] MLFlow Support
[21:37] Notebooks to production debates  
[25:42] Companies tackling Notebooks for production
[27:40] MLOoops stories
[31:03] Opinionated MLOps productionizing in a good way
[40:23] Xiangrui's MLOps Vision
[44:47] Lightning round
[48:45] Wrap up

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