According to Venturebeat, 87% of machine learning projects never make it to production, ever wondered why? Well, effectively deploying machine learning models is more of an art than science! Learn more about how well your machine learning model is performing and how to build a Test-Driven-Development environment which takes care of FATE (Fairness, Accountability, Transparency, Ethics) in ML algorithms and data in our upcoming free live-online webinar with the fabulous Rashmi Nagpal. When? Wednesday, August 9 3:00 PM CEST/ 9:00 AM EST How? Sign up to our free webinar series. (The webinar is held in crowdcast. With signing up to the list, you will receive a registration confirmation incl. a link to join shortly before the webinar starts.)
Session Abstract: Over the past decade, we have witnessed a renaissance around Artificial Intelligence systems, a paradigm shift from computerized deduction to computerized induction. But the issue with these systems is that they are inherently complex, have fuzzy boundaries, rely heavily on data dependencies, and fundamentally change due to variation in the actual world data. Thus, adopting test-driven development in these systems could be tricky. In this talk, I will share my learnings on how to build effective practices to deploy machine learning models into production. Also, I will cover how to test the decisions made by the machine learning model using SOLID principles.
About the speaker: Rashmi Nagpal is a Software Engineer with 3+ years of industry experience. She is also a Research Affiliate at the University of San Francisco, CA, working in sociolinguistics. She is passionate about exploring FATE (Fairness, Accountability, Transparency, and Ethics) in NLP algorithms and bringing research and academia to the industry. She is a leader at Women Who Go, wherein she is actively involved in bridging the gender gap in Science, Technology, Engineering, and Mathematics (STEM) fields.