Accelerate Machine Learning with a Unified Analytics Architecture

About the Authors

Ben Epstein was the machine learning lead at Splice Machine, an end-to-end MLOps and feature store platform. As ML lead, Ben was responsible for bringing to market a full stack, user-facing ML platform, supporting large-scale data and ML systems spanning from data ingestion to production model monitoring. With a focus on real-time, distributed use cases, the platform was built on Apache Spark, Kubernetes, MLFlow, and a custom-built database model deployment architecture. Ben has extensive experience designing end-to-end ML systems for scale, supporting petabytes of data, and he recognizes the challenges that come with it in today’s ML tooling landscape. Today, he works as a founding engineer focusing on data-centric AI, building systems to help data scientists and machine learning engineers derive better data for their models. He also works with Washington University in St. Louis as an adjunct professor on a cloud computing and big data course, focusing on real-world use cases and skill sets.

Paige Roberts (@RobertsPaige) has worked as an engineer, trainer, support technician, technical writer, marketer, product manager, and a consultant in the last 25 years. She has built data engineering pipelines and architectures, documented and tested open source analytics implementations, spun up Hadoop clusters, picked the brains of stars in data analytics, worked in different industries, and questioned a lot of assumptions. She has worked for companies like Pervasive, ...