Abstract: 

Learn how to seamlessly use Julia for your machine learning tasks — even if they are within an air-gapped secure environment or require an entire cluster for their computation. Julia Computing’s products take the guesswork out of building scalable solutions and can be used by data scientists and engineers with little to no knowledge of how such systems need to be architected. Develop and collaborate on your pipelines locally and deploy them into a scalable robust on-demand cluster with a single click.

Bio: 

Matt Bauman is the Director of Applications Engineering at Julia Computing. He has developed machine learning pipelines in C, Matlab, Python, and now Julia, targeting problems ranging from neural engineering at the University of Pittsburgh (where he received his PhD in 2018) to social services at the University of Chicago’s Center for Data Science and Public Policy. He’s been a contributor to the Julia language and its packages for over 6 years and now helps guide Julia Computing’s products and trainings.