Sketchnotes from TWiML&AI #115: Scaling Machine Learning at Uber with Mike Del Balso

March 6, 2018
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(This article was first published on Shirin's playgRound, and kindly contributed to R-bloggers)

These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Scaling Machine Learning at Uber with Mike Del Balso:

Sketchnotes from TWiMLAI talk #115: Scaling Machine Learning at Uber with Mike Del Balso

Sketchnotes from TWiMLAI talk #115: Scaling Machine Learning at Uber with Mike Del Balso

You can listen to the podcast here.

In this episode, I speak with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. Mike and I sat down last fall at the Georgian Partners Portfolio conference to discuss his presentation “Finding success with machine learning in your company.” In our discussion, Mike shares some great advice for organizations looking to get value out of machine learning. He also details some of the pitfalls companies run into, such as not have proper infrastructure in place for maintenance and monitoring, not managing their expectations, and not putting the right tools in place for data science and development teams. On this last point, we touch on the Michelangelo platform, which Uber uses internally to build, deploy and maintain ML systems at scale, and the open source distributed TensorFlow system they’ve created, Horovod. This was a very insightful interview, so get your notepad ready! https://twimlai.com/twiml-talk-115-scaling-machine-learning-uber-mike-del-balso/

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