Rajiv Sambasivan

I work in data science and ML, I am building a knowledge management library for data science. https://github.com/rajivsam/KMDS and https://github.com/rajivsam/kmds... and here is the tl;dr video: https://www.youtube.com/watch?v=ckr8YQJxF9I

There are two guys who have written extensively about adoption of AI for business use cases. One is Foster Provost, the other is Eric Siegel. You should check out thier recommendations and guidelines for picking and measuring AI project success. The TL;DR version is to start with the business outcome you want to change with your AI idea and then have concrete, clear and interpretable measures...

A lot of data science work is incremental and experimental. Any reasonable data science project requires choices that are mode based on the characteristics of the use case, and, there are many of these that accumalate over time. When it is time to rebuild the model because the data has changed this is forgotten. KMDS fixes this problem. See the kmds wiki for recipes to use kmds:...