As more applications harness the power of real-time data, it’s important to architect and implement a data stack to meet the broad requirements of operational ML and be able to seamlessly integrate neural embeddings into applications.
Real-time ML requires more than just deploying ML models to production using MLOps tooling; it requires a fast and scalable operational database that easily integrates into the MLOps workflow. Milliseconds matter and can make the difference in delivering fast online predictions whether it’s personalized recommendations, detecting fraud, or figuring out the most optimal food delivery route.
Attend this session to explore how a modern data stack can be used for real-time operational ML and building AI-infused applications. The session will over the following topics:
Emerging architectural components for operational ML such as the online feature store for real-time serving.
Operational excellence in managing globally distributed ML data and feature pipelines
Foundational data types of Redis including the representation of data using vector embeddings.
Using Redis as a vector database to build vector similarity search applications.
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