How to Go From Data Science to Data Operations
How to Go From Data Science to Data Operations

Abstract: 

According to Google developers, "Only a small fraction of real-world ML systems are composed of the ML code. The required surrounding infrastructure is vast and complex". This talk focuses on how to simplify that technical environment, how to organize for success, and what to measure -- DataOps. By focusing on DataOps your teams will be able to deliver faster, with higher quality, using the tools that they love.

This session will cover three major topics:

1) Seven Shocking Steps: We will outline the seven Shocking Steps to create a Data Operations environment including how to: orchestrate, deploy, monitor quality, test data and code, encourage reuse, version control, branch & merge, manage environments.

2) How to Organize: We will look at best practices on how to organize your people.

3) How to Measure: We will show to how to measure the effectiveness of your Data Operations through key metrics.

Bio: 

Gil Benghiat is a co-founder of DataKitchen, a company on a mission to enable analytic teams to deliver value quickly and with high quality. Gil’s career has always been data-oriented starting by collecting and displaying network data at AT&T Bell Laboratories, managing data at Sybase, collecting and cleaning clinical trial data at PhaseForward, integrating pharmaceutical sales data at LeapFrogRx, and liberating data at Solid Oak Consulting. Gil holds an MS in computer science from Stanford University and a BS in applied mathematics and biology from Brown University. He is working on hiking the 67 New England 4,000' peaks.

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

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