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In this talk, I describe some recent advancements in Streaming ML and AI Pipelines to enable data scientists to rapidly train and test on streaming data - and ultimately deploy models directly into production on their own with low friction and high impact.

With proper tooling and monitoring, data scientist have the freedom and responsibility to experiment rapidly on live, streaming data - and deploy directly into production as often as necessary. I’ll describe this tooling - and demonstrate a real production pipeline using Jupyter Notebook, Docker, Kubernetes, Spark ML, Kafka, TensorFlow, Jenkins, and Netflix Open Source.