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Scaling AI Applications with Pinecone and Kubernetes

By Roie Schwaber-Cohen

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.

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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.

Chapter 1
Introduction
Introducing the problem scope and the driving use case
Chapter 2
Ingestion Microservices
A deeper dive into the ingestion microservices

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Chapter 3

A step-by-step walkthrough of the workflow, shedding light on the intricacies of the labeling system.

Chapter 4

An exploration of how Kubernetes supports scaling and managing the system, including deployment strategies and handling service communication.