Scaling AI Applications with Pinecone and Kubernetes
Scaling AI applications comes with it's own set of challenges - but it also shares a lot in common with other kinds of production scale applications. In this series, we'll explore these challenges and review a reference architecture for a distributed AI application built to scale.
Introduction
Scaling AI applications comes with it's own set of challenges - but it also shares a lot in common with other kinds of production scale applications. In this series, we'll explore these challenges and review a reference architecture for a distributed AI application built to scale. We'll apply a microservices architecture with Kubernetes to demonstrate a concrete implementation to solve these challenges.
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Introduction
Introducing the problem scope and the driving use case
Chapter 02Ingestion Microservices
A deeper dive into the ingestion microservices
A step-by-step walkthrough of the workflow, shedding light on the intricacies of the labeling system.
An exploration of how Kubernetes supports scaling and managing the system, including deployment strategies and handling service communication.
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