Abstract: Turning ML into magical products often requires complex distributed systems that bring with them a unique ML-specific set of infrastructure problems. why, what, and how of building a production ML platform. Bhairav will demonstrate how to use Kubeflow to build and deploy ML. This talk will focus on all the engineering aspects involved in Machine Learning at scale. A common warning shared with aspiring Data Scientists & ML engineers is that 90% of the work is about gathering, cleaning and validating data plus deploying and monitoring models. Yet for a long time most of the open source ML tooling focused on the modeling part.
Bhairav will first give an overview of the different ML Engineering frameworks out there, both open and closed source. We will then focus in on Kubeflow Pipelines and TFX (Tensorflow Extended), both of which are open source, by giving an end-to-end example highlighting why these frameworks are incredibly powerful. This example includes transforming and validating the data, training a model in a distributed way, validating and monitoring model performance and last but not least deploying the model.
Bio: Bhairav Mehta is Senior Data Scientist with extensive professional experience and academic background. Bhairav works for Apple Inc. as Sr. Data Scientist.
Bhairav Mehta is experienced engineer, business professional and seasoned Statistician / programmer with 19 years of combined progressive experience working on data science in electronics consumer products industry (7 years at Apple Inc.), yield engineering in semiconductor manufacturing (6 years at Qualcomm and MIT Startup) and quality engineering in automotive industry (OEM, Tier2 Suppliers, Ford Motor Company) (3 years). Bhairav founded a start up DataInquest Inc. in 2014 that is specialized in training/consulting in Artificial Intelligence, Machine Learning, Blockchain and Data Science.
Bhairav Mehta has MBA from Johnson School of Management at Cornell University, Masters in Computer science from Georgia Tech (Expected 2018), Masters in Statistics from Cornell University, Masters in Industrial Systems Engineering from Rochester Institute of Technology and BS Production Engineering from Mumbai University.
Data Science Manager | Apple Inc.
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