Organizations of all kinds are now seeking strong machine learning (ML) capabilities to stay competitive. Should they build or buy, and what does it depend on? What is the likely ML expertise obtainable? Where should it live organizationally? How mature are available ML tools, and what is their true ownership cost?
In this webinar, we will describe four critical steps of developing machine learning capabilities, determine the true cost of ownership of machine learning tools, outline how successful organizations have set up their data science capabilities organizationally, and pinpoint a spectrum of hybrid strategies for creating in-house machine learning capabilities spanning the build vs. buy extremes.
Dr. Gray is CTO at Skytree and Associate Professor in the College of Computing at Georgia Tech. His work has focused on algorithmic techniques for making machine learning tractable on massive datasets. He began working with large-scale scientific data in 1993 at NASA’s Jet Propulsion Laboratory in its Machine Learning Systems Group. He recently served on the National Academy of Sciences Committee on the Analysis of Massive Data, as a Kavli Scholar, and a Berkeley Simons Fellow, and is a frequent advisor and speaker on the topic of machine learning on big data in academia, science, and industry.