Abstract: As businesses rush to adopt and operationalize data-intensive applications, the myriad organizational and technical gaps have led to the emergence of a variety of misguided concepts, narratives, and approaches. In this talk, Peter investigates a few of these, including the concept of "citizen data science" and the "open source head fake" from cloud vendors, and argues for a holistic treatment of code+data for enterprise machine learning.
Bio: Peter has a B.A. in Physics from Cornell University, and has been developing commercial scientific computing and visualization software for over 15 years. He has software design and development experience across a broad variety of areas, including 3D graphics, geophysics, financial risk modeling, large data simulation and visualization, and medical imaging.Peter’s interests in the fundamentals of vector computing and interactive, large-scale visualization led him to co-founding Continuum Analytics. As CTO, Peter is the technology visionary and leads the product engineering team for the Anaconda platform as well as open source projects including Bokeh and Blaze. As a creator of the PyData conference, he also devotes time and energy to growing the Python data community by advocating, teaching, and speaking about Python at conferences worldwide.