Soumanta wouldn't claim they've reached where they want to and they're still learning, so he's happy sharing successes as well as failures at Yugen.ai.
// Abstract
Determining Minimum Achievable Goals helps Yugen.ai ensure a significant amount of focus on value-added and impact before diving deep into solutions & building ML Systems. In this episode, Soumanta discusses Balancing ML Development vs Ops and Monitoring efforts while scaling plus their focus on improvements in small sprints.
Soumanta wouldn't claim they've reached where they want to and they're still learning, so he's happy sharing successes as well as failures at Yugen.ai.
// Bio
Soumanta is a Co-founder at Yugen.ai, an early-stage startup in the Data Science and MLOps space.
We imagine the future to be shaped by the convergence and simultaneous adoption of Algorithms, Engineering and Ops, and Responsible AI. Our mission is to help effectuate and expedite the same for our client partners by creating large-scale, reliable, and personalized ML Systems.
// Relevant Links
A blog Soumanta wrote when Yugen turned one https://medium.com/swlh/yugen-ai-turns-one-1089f3bf169
Presentation, ML REPA 2021 Title of the Talk - Reducing the distance between Prototyping and Production, Why obsessing over experimentation and iteration compounds ROIs
Slides - https://drive.google.com/file/d/1J9Cv6IPPkGpOTq8Xl_AQCKaR0-pKMUmA/view?usp=sharing
Video - https://youtu.be/4PEbgQTw1W0
--------------- ✌️Connect With Us ✌️ -------------
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Connect with Soumanta on LinkedIn: www.linkedin.com/in/soumanta-das/
Timestamps:
[00:00] Introduction to Soumanta Das
[00:24] What's Yugen.ai's name all about?
[02:02] Starting during the pandemic
[05:13] Determination to continue during the pandemic
[08:02] State of the art in Yugen.ai and its future
[11:32] Time to value defining ML to a business
[13:01] Building a strong ML engineering culture
[19:06] Data scientists patterns
[20:00] Helper functions
[22:45] Code review
[25:32] Repeatable use cases
[27:48] Minimum achievable goals
[30:30] Production management goals
[34:30] Use cases and System design document
[36:20] Practices that helped Yugen.ai build ML systems
[40:05] Growing pains in the scaling process
[43:54] Yugen.ai war stories
[46:50] Unrealizing there's something wrong and there's actually something wrong
[48:10] Data observability tools
[49:42] Hands-on deck
