AnnouncementIntroducing import from object storage for more efficient data transfer to Pinecone serverlessLearn more

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.

Share:

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

New chapters coming soon!

Get email updates when they're published:

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.