Bay Area MLflow Meetup with RedisAI, Nvidia & Databricks

Zoom link will be posted 24hr before the event.

Talk 1: Taking Deep Learning to Production with MLflow & RedisAI
Presenter: Sherin Thomas, RedisAI
Abstract: Taking deep learning models to production and doing so reliably is one of the next frontiers of MLOps. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution for a model serving microservice. RedisAI is shipped with several cool features such as support for multiple frameworks, CPU and GPU backend, auto batching, DAGing, and soon will be with automatic monitoring abilities. In this talk, we'll explore some of these features of RedisAI and see how easy it is to integrate MLflow and RedisAI to build an efficient productionization pipeline.

Bio: Sherin Thomas is a software engineer, deep learning consultant, author, and international speaker who is currently working at tensorwerk, a deep learning infrastructure company. He helps to build tools for an end-to-end deep learning pipeline such as RedisAI - a high performant deep learning runtime, Hangar, and Stockroom - version control for software 2.0. He is also an active contributor to deep learning ecosystem tools such as PyTorch, MLflow, etc. He is an author, speaker, and co-created fullstackengineering.ai, a deep learning mentorship platform.

Talk 2: Accelerated Machine Learning with RAPIDS and MLflow, Nvidia/RAPIDS
Abstract: We will introduce RAPIDS, a suite of open source libraries for GPU-accelerated data science, and illustrate how it operates seamlessly with MLflow to enable reproducible training, model storage, and deployment. We will walk through a baseline example that incorporates MLflow locally, with a simple SQLite backend, and briefly introduce how the same workflow can be deployed in the context of GPU enabled Kubernetes clusters.
Bios:
John Zedlewski is the director of GPU-accelerated machine learning on the RAPIDS team. Previously, he worked on deep learning for self-driving cars at NVIDIA, deep learning for radiology at Enlitic, and machine learning for structured healthcare data at Castlight. He has an MA/ABD in economics from Harvard with a focus on computational econometrics and an AB in computer science from Princeton.

Devin Robison is a data scientist on the RAPIDS team. Prior to joining RAPIDS, he worked as a solutions architect for NVIDIA focusing on ML/DL in the cloud, systems architecture and erasure coding at Oracle, and systems software / Linux driver engineering for FusionIO. He has a BS/BE in mathematics and computer engineering from the University of Tennessee, MS in computational engineering and science from the University of Utah, and is currently pursuing an MS in data science from UC Berkeley.

Talk 3: New Developments in MLflow
Presenter: Corey Zumar, Databricks
Abstract: In the last several months, MLflow has introduced significant platform enhancements that simplify machine learning lifecycle management. Expanded autologging capabilities, including a new integration with scikit-learn, have streamlined the instrumentation and experimentation process in MLflow Tracking. Additionally, schema management functionality has been incorporated into MLflow Models, enabling users to seamlessly inspect and control model inference APIs for batch and real-time scoring. In this session, we will explore these new features. We will share MLflow’s development roadmap, providing an overview of near-term advancements in the platform.

Bio: Corey Zumar is a software engineer at Databricks, where he is working on machine learning infrastructure and APIs for managing the machine learning lifecycle with MLflow. He holds a master’s degree in computer science from UC Berkeley, where he was one of the lead developers of Clipper - an open-source project and research effort focused on high-performance model serving.