InfoQ Podcast - Building Data Science Capability

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In this podcast, recorded live at QCon.ai, Principal Technical Advisor & QCon Chair Wes Reisz and InfoQ Editor-in-chief Charles Humble chair a panel discussion with Stephanie Yee, data scientist at StitchFix, Matei Zaharia, professor of computer science at Stanford and chief scientist at Data Bricks, Sid Anand, chief data engineer at PayPal, and Soups Ranjan, director of data science at CoinBase.

Why listen to this podcast:

 - Before you start putting a data science team together make sure you have a business goal or question that you want to answer;  If you have a specific question, like increasing lift on a metric, or understanding  customer usage patterns, you know where you can get the data from, and you can then figure out how to organise that data.
 - You need to make sure you have the right culture for the team - and find people who are excited about solving the business problems and be interested in it.  Also look at the environment you are going to provide. 
 - Your first hire shouldn’t be a data scientist (or quant).  You need support to productionise the models - and if you don’t have a colleague to help productionise it then don’t hire the quant first.
 - Given the scarcity of talent it is worth remembering that Data Scientists come from a variety of different backgrounds - Some people have computer science backgrounds, some may be astrophysicists or neuroscientists who approach problems in different ways.
 - There are two common ways to structure a data science team: one is a vertical team that does everything, the other, more common in large companies, is when you have a separate data science team and an infrastructure team.

More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2Jym1RI

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InfoQ Podcast

Show Notes

Building a Data Science Capability with Stephanie Yee, Matei Zaharia, Sid Anand and Soups Ranjan

Probabilistic Programming has been discussed as a programming paradigm that uses statistical approaches to dealing with uncertainty in data as a first class construct. On today’s podcast, Wes talks with Mike Lee Williams of Cloudera’s Fast Forward Labs about Probabilistic Programming. The two discusses how Bayesian Inference works, how it’s used in Probabilistic Programming, production-level languages in the space, and some of the implementations/libraries that we’re seeing.

Key Takeaways
* Federated machine learning is an approach of developing models at an edge device and returning just the model to a centralized location. By taking the averages of the edge models, you can protect privacy and distribute processing of building models.
*Probabilistic Programming is a family of programming languages that make statistical problems easier to describe and solve.
*It is heavily influenced by Bayesian Inference or an approach to experimentation that turns what you know before the experiment and the results of the experiment into concrete answers on what you should do next.
* The Bayesian approach to unsupervised learning comes with the ability to measure uncertainty (or the ability to quantify risk).
* Most of the tooling used for Probabilistic Programming today is highly declarative. “You simply describe the world and press go.”
* If you have a practical, real-world problem today for Probabilistic Programming, Stan and PyMC3 are two languages to consider. Both are relatively mature languages with great documentation.
* Prophet, a time-series forecasting library built at Facebook as a wrapper around Stan, is a particularly approachable place to use Bayesian Inference for forecasting use cases general purpose.

Mike Lee Williams on Probabilistic Programming, Bayesian Inference, and Languages like PyMC3

We spoke with Jonathan Frankle, a PhD student at MIT and coauthor of a seminal paper outlining a technique, known as the “The Lottery Ticket,” hypothesis that promises to help advance our understanding of why neural networks, and deep learning, works so well.

MIT’s Jonathan Frankle on “The Lottery Hypothesis”

MIT’s Jonathan Frankle on “The Lottery Hypothesis” - Ep. 115

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