Data Science Double Header: DevOps Lessons & Deploying ML in the Enterprise

Name: Data Science Double Header: DevOps Lessons & Deploying ML in the Enterprise
Start: 2020-01-15T18:30:00-05:00
End: 2020-01-15T20:30:00-05:00
Location: GWU, Funger Hall, Room 103

Hosted By

Tommy J. and 2 others

Data Science Double Header: DevOps Lessons & Deploying ML in the Enterprise

Details

Talk 1:
Reflections on a Year Spent Talking to Data Scientists about DevOps

As a Solutions Engineer at RStudio, I spend a good deal of time helping data scientists advocate for R within their organizations. These people are fully committed to ushering in better data practices and adopting the best tools and infrastructure for their team. Unfortunately, even highly motivated individuals can run into roadblocks when trying to get R recognized as an analytic standard. Commonly issues stem from a lack of management and governance around open source software; especially when IT is unfamiliar with requisite core competencies. In this talk, I present DevOps as a framework for understanding and navigating these kinds of organizational challenges.

Kelly O’Briant is a solutions engineer at RStudio interested in configuration and workflow management with a passion for R administration.

---------------------------
Talk 2:
Deploying and Managing Machine Learning in Enterprise Environments

You've Built and Trained a Model. Now What?
An Overview of the ML lifecycle–what your company needs at every stage, from data collection to resource management, and how that impacts your deployment choices. We’ll then discuss custom deployment solutions from different industries and what you can learn from their success (and failures).

Brendan Collins is the lead Solutions Engineer for Algorithmia’s east coast enterprise customers. He has worked in financial enterprise infrastructure for more than a decade, with groups ranging in size from the largest financial institutions in the world to community banks. Brendan has a true passion for helping enterprises use machine learning and data science to solve cutting edge problems, as well as a personal interest in serverless technologies of all shapes and sizes.