The AI Products track demonstrates the intersection of applied DS/ML methods in product form, covering topics such as full stack product development of data-oriented products, product-based implementations of new research methods, and machine-learning powered features inside products (i.e. recommendation systems, NLP based features, scoring, etc.). This is a product-focused track, where talks are usually given by technical founders or product managers with a technical background.
Daniel Francisco is Product Management Lead for natural language understanding at Google AI. He's responsible for ensuring that Google's state of the art research is built into products across Google to deliver amazing user experiences. Prior to Google, Daniel was Product Management Lead for Search Product Experience at Facebook. He's also served as Chief of Staff to the CEO at LinkedIn and was part of the team that navigated the company through hypergrowth as the company grew from 200 employees to 6000+, 18M members to 400M+, and through their IPO. Daniel has also held roles at RelateIQ, Salesforce, and Microsoft.
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