Supercharge your cloud-native applications with Generative AI
What if your AI knew your company's private data? Learn how to build cost-effective, enterprise-aware models using the RAG pattern.
#1about 7 minutes
The developer's journey for building AI applications
An overview of the AI application lifecycle from prototyping to production and the advantages of using local models for cost, data privacy, and customization.
#2about 10 minutes
Prototyping AI applications locally with Podman AI Lab
A hands-on demonstration of using Podman AI Lab to run local models, start applications from recipes, and integrate AI into both Python and Java code.
#3about 5 minutes
Using RAG to enhance models and scale to production
An explanation of the Retrieval-Augmented Generation (RAG) pattern for adding custom data to models and an overview of the MLOps stack needed for enterprise deployment.
#4about 11 minutes
Deploying a RAG-enabled chatbot on a Kubernetes platform
A complete walkthrough of deploying a RAG-enabled application, including ingesting documents into Elasticsearch, serving a model, and running the final container on OpenShift AI.
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