20 Wed 05:50 PM – 07:10 PM IST
Despite growing teams and budgets, very few Machine Learning (ML) models reach the production stage. What is productionization and what makes it difficult? How does it relate to MLOps? How should one plan for production ML? What kinds of systems and process should be in place for ensuring successful delivery? What does the team structure and skillsets look like?
In this forum, hosts Venkata Pingali and Indrayudh Ghoshal of Scribble Data converse with data science practitioners. The hosts delve into their viewpoints and war stories for an interesting, insightful take on making data science work for better business impact. As there are no rule books for how to do data science, these experiential conversations help in evaluating choices and approaches, and avoidance of expensive mistakes.
Summary of the session:
This discussion tackled how to set up Machine Learning (ML) projects for success. Speakers included Goda Ramkumar of Swiggy and Srujana Merugu, a data science consultant + Anthill Inside speaker alumnus. A quick summary of this session is as follows:
Key actionable for organizations are:
This summary is compiled by participant Manu Raveendran and Venkata Pingali with copy-editing support from The Fifth Elephant team.
Participants’ questions from this session, and ongoing discussion continues on https://hasgeek.com/fifthelephant/making-data-science-work/comments
Reading references for this session:
About the curators: Venkata Pingali and Indrayudh Ghoshal of Scribble Data have curated this session. Scribble Data is a Bangalore/Toronto startup, active in the data community.
About the series producer: The Fifth Elephant is platform for practitioners working with data (engineering, to application of data science for different use cases) to showcase their work and to collaborate.
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