The Ops in MLOps - Process and People // Shalabh Chaudri // MLOps Podcast #146

MLOps Coffee Sessions #146 with Shalabh Chaudri, The Ops in MLOps - Process and People co-hosted by Abi Aryan.

// Abstract
Shalabh talks through their newfound appreciation for the MLOps perspective from a customer success standpoint. Shalabh's emphasis on setting realistic expectations and ensuring the delivery of promised value adds is particularly valuable.    

Generally, this episode provides a unique and insightful perspective on MLOps from the lens of customer success.

// Bio
Shalabh has worked in the MLOps domain since 2020 at Algorithmia and Union AI. His experience spans startups and small and large public companies. He has 10+ years of experience in the design, delivery, adoption, and business value realization of B2B infrastructure and platform solutions.

// MLOps Jobs board  
https://mlops.pallet.xyz/jobs

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

// Related Links
https://www.union.ai/

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/
Connect with Shalabh on LinkedIn: https://www.linkedin.com/in/shalabhchaudhri/

Timestamps:
[00:00] Shalabh's preferred coffee
[01:18] Takeaways
[02:57] Huge shout out to Union AI!
[03:46] Reviews
[05:26] Shalab's journey
[07:00] The people and process of MLOps
[10:25] Accuracy measures and Multiple Stakeholders
[13:01] UnionAI's success where others fall short
[14:45] Legacy equipment
[17:06] Legacy tools versus open source
[19:27] Cataloging solution
[22:51] Stakeholders and maturity levels
[24:26] People and Process in MLOps
[29:00] Collaboration for Machine Learning
[31:08] Overcoming challenges
[34:17] AI and leadership decision-making
[35:33] Legacy Companies and AI
[39:39] Common pitfalls  
[42:24] Neglecting ROI
[46:25] Speaking to each level
[49:50] Being realistic
[51:29] Becoming a champion
[53:08] Transitioning to machine learning
[55:25] Customer's Skill and Success needed in ML  
[57:46] Different sizes of companies