Everybody needs a bit of science in their data

In this session, we’ll look at the key challenges of doing data science at scale – not just the mechanics of processing big data but the things that matter in putting our data to work.  Things like how to work on that data in a multi-disciplinary team, how to setup and evaluate experiments and how to move from dev to test and production so those insights can be put to work across the business as needed.  I will be showing how we do this at Microsoft, which might sound proprietary but actually is mainly about how we weave open source technologies together – technologies like Spark and Python, and containers.

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