Topic name: Incentives(rewards) for your open source contributions
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If your open-source project makes consistent releases, and offers some sort of support lifecycle (say last 2 major releases are supported, or you support only the latest Python release) - documenting these becomes extremely important, especially for the folks responsible for using your project securely.
The talk will cover best practices around documenting your release cadence, processes, EOL dates, versioning schemes, and various gotchas. There will be lots of examples to showcase what is good vs bad (and why).
The talk comes out of my experience of creating endoflife.date as an open-source community project, and documenting the lifecycles and EOL dates of more than 100 products.
The questions in ML domain have changed from whether we can deliver ML model to whether we can deliver usecases viably and scalably. In this talk, I will share the challenges we saw in scaling the usecases (not data volume) such as lack of clarity and operational complexity, and discuss the tradeoffs that we have to make to increase adoption of ML.
Nature: Introductory, Conceptual, Opensource friendly, 10 mins
Speaker: Dr. Venkata Pingali, Co-Founder of Scribble Data (an MLOps firm with offices in Toronto/Bangalore)
Suggestion: Please change “June '22” to “June 2022” everywhere. It’s quite confusing to see “June '22” in my inbox and wonder if I was wrong about the date.