End-to-end ML streaming with Kubeflow, Kafka, and Redis at scale

With ubiquitous ML models, model serving and pipelining is more important now. Comcast runs hundreds of models at scale with Kubernetes and Kubeflow. Together with other popular open source streaming platforms such as Apache Kafka and Redis, Comcast invokes models billions of times per day while maintaining high availability guarantees and quick deployments. Join Nick Pinckernell to learn how.

At scale, large solutions are sometimes required to tackle even the smallest tasks, and ML is no different. Comcast is building architectures to handle end-to-end ML pipelining and deployments. Nick Pinckernell outlines a solution that demonstrates configuration-based, continuously integrated and deployed solutions to handle data transformation, normalization, and model serving. This is accomplished using a range of tools and frameworks such as Kubernetes, Apache Spark, and more. It all starts with a large Apache Spark environment used by many researchers to explore and train models. The researchers are then empowered to develop simple or complex model graphs and deploy themselves using Kubeflow and Seldon Core. Data streams into these models using Apache Kafka with windowing and aggregation handled by Redis. You’ll gain an understanding of the architecture, configuration, and technologies that are involved in making this happen at scale. Nick provides specific examples and flows of requests to example models to demonstrate all the necessary components and configuration.