Coffee Sessions #56

A Few Learnings from Building a Bootstrapped MLOps Services Startup

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.

Take-aways

a) The concept & importance of Minimum Achievable Goals b) The need for continuous experimentation & iteration to achieve goals c) How effective system design can position an ML team for success, especially in the remote WFH culture

In this episode

Soumanta Das

Soumanta Das

Co-founder, Yugen.ai

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.

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Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Vishnu Rachakonda

Vishnu Rachakonda

Host

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.