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Free Webinar on Introduction to Scalable Machine Learning
Starts at:
Wednesday, June 23 @ 2:30 p.m. UTC
Ends at:
Wednesday, June 23 @ 4:30 p.m. UTC
Wednesday, June 23 @ 2:30 p.m. UTC
Wednesday, June 23 @ 4:30 p.m. UTC
About the Event
Machine Learning usage in the industry is fairly nascent with most practitioners gaining expertise through online courses and working mostly within the confines of a colab. When the same practitioner is tasked with designing and deploying their first ML system, they often make the mistake of focusing entirely on the model development part and their local development environment where training rather than inference is the bottleneck; resulting in culture shock and a steep learning curve getting the model deployed to production
The objective of this webinar is to prepare the practitioner for the inevitable culture shock and minimize the learning curve by instilling production best practices that tech giants adhere to. We will present a detailed overview of the design and architectural choices behind Uber's Michelangelo: The full-stack ML ecosystem empowering hundreds of engineers and analysts to deploy thousands of models daily.
A certificate of participation will be provided for this session.
About Presenter
About Presenter
Venkat started his professional career as a trading systems engineer at the hedge fund D.E. Shaw where he developed a fixation of hyper-efficient code and Perl haikus. He moved to Google India when they started engineering operations in Hyderabad and migrated to work at Google, Mountain View, California in 2007. Venkat spent four years on the core Google Web Search UI team where he received 3 patents for building the critical RPC service that renders the first HTTP header chunk for all Google properties with a 99th percentile latency of ~7ms @ a peak of 1 million queries per second. Venkat was also the performance lead for the core Java framework used by most Google properties and built expertise in creating frameworks for other engineers.
Venkat moved to Google, Brasil in 2014 and has been there for 4 years with a 3-year break in between as a partner at a Deep Learning ad-tech startup which was acqui-hired by the Brasilian delivery giant Loggi. He currently divides his time combating the decline of Moore's Law for Google Search and developing transformer language models for improving query suggestions.