Sinan is a former lecturer of Data Science at The Johns Hopkins University and the author of 4 textbooks about Data Science and Machine Learning. He is the founder of the acquired company Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. Sinan is currently the Director of Data Science at Directly in San Francisco, CA.
Conversational AI systems suffer from two forms of decay: concept drift, when interpretation of data changes, and data drift, when the underlying distributions of the data change. These forms of decay cause static AI models to degrade, often within days of creation. Using a combination of state-of-the-art NLP transfer learning tasks, a modern data pipeline using Databricks, and a network of experts completing distributed gamified data labelling tasks, Directly is able to provide a more effective and powerful end-to-end machine learning and conversation automation solution than systems that train static models and then expect performance to stay steady over time. This talk will dive into the specific mechanics required to create and maintain a living, breathing AI ecosystem, including lessons learned by creating a global network of experts and the pitfalls of training/hosting/versioning high-performance dynamic AI. Both technical and non-technical attendees are highly encouraged to participate in this talk. We will have deep dives into AI code/theory that will always be backed by an underlying real business use-case and performance metrics.