Data Science in Production

There is a lot that goes behind developing a model such as data cleaning, analysis, statistics, modeling, accuracy analysis etc. Even with all that, developing a model is just the tip of the iceberg when it comes to delivering a machine learning solution in production. I spoke at OSCON 2018 conference on the engineering practices that enable scalable and sustainable machine learning systems.

I hope to find some time to blog about this topic in detail at some point. Meanwhile you can find my find my slides here: