Taking Deep Learning to Production with MLflow and RedisAI

Explore the integration of MLflow and RedisAI for efficient deep learning model deployment in this 30-minute talk from Databricks. Learn how to leverage Redis modules and C APIs to create a reliable runtime for deep learning workloads, transforming Redis into a model serving microservice. Discover RedisAI's key features, including multi-framework support, CPU and GPU backend, auto batching, and DAGing. Follow along as the speaker demonstrates how to build a streamlined productionization pipeline, addressing the challenges of taking deep learning models to production reliably. Gain insights into production strategies, requirements, and the practical implementation of RedisAI in your MLOps workflow.

Intro
Hanger
MLflow
Production Strategies
Production Requirements
RedisAI
Getting RedisAI
Replication
Features
Demo
Blog
Outro