Tuesday, November 30, 6:00pm - 7:00pm (EST)
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R in Retail & E-Commerce:
ML Ops - Machine Learning as an Engineering Discipline
Presentation by Suteja Kanuri
“Only 22 percent of companies using machine learning have successfully deployed a model."
What makes it so hard? And what do we need to do to improve the situation?
ML Ops is a set of practices that combines Machine Learning, DevOps, and Data Engineering - while deploying and maintaining ML systems in production reliably and efficiently. There are various maturity levels of ML Ops based on the industry and it's important to have an awareness of an ML Ops toolkit for Machine Learning teams to succeed.
Suteja is based out of Singapore and started her career in the data industry. She has been an Engineering Manager for the last 3 years across two industries- Banking and E-commerce. She has immense experience working with Machine Learning Teams and is well versed with ML Ops practices across multiple industries. She has nurtured and managed a 15 member team of ML Ops which comprised of Machine Learning Engineers and data engineers.
Rachael Dempsey, firstname.lastname@example.org