NVIDIA: Introduction to Deploying RAG Pipelines for Production at Scale (English)

Learn how to set up and manage scalable, production-ready Retrieval-Augmented Generation (RAG) pipelines using Kubernetes, NVIDIA NIMs, Helm, Prometheus, and Grafana.

Certificate

Upon completion of this training, you will receive the certificate of participation ‘NVIDIA: Introduction to Deploying RAG Pipelines for Production at Scale (English)’ by Computrain. 

Your result

When completing this training, you will be able to:

• Deploy RAG pipelines on Kubernetes using Helm and the NVIDIA NIM Operator
• Use NVIDIA NIMs for scalable, containerized Large Language Models (LLMs) and embedding models
• Connect, customize, extend, and autoscale application components
• Monitor application performance with Prometheus and Grafana
• Perform load testing and optimize RAG pipelines

Target audience

This training is intended for:

• Machine Learning Engineers who want to deploy RAG applications in production environments
• AI developers who want to implement scalable AI services
• DevOps Engineers who manage Kubernetes environments running AI workloads
• Software Architects who design robust AI architectures
• Technical Leads who want to scale Proof-of-Concepts to enterprise production systems

Prerequisits

• Basic knowledge of Kubernetes and container orchestration
• Experience with AI models and LLMs is a plus
• Familiarity with Helm, Prometheus, and Grafana is helpful but not required

Study materials

• Access to interactive labs in a Kubernetes cluster
• Step-by-step instructions through the digital learning environment
• Technical documentation on NIMs, Helm, monitoring tools, and scaling techniques

Approach

The training is practical and interactive. You will work hands-on in a Kubernetes cluster and learn to:

• Set up an infrastructure
• Deploy and configure a RAG pipeline
• Set up monitoring
• Implement autoscaling
• Optimize performance

All components are supported with clear explanations, demonstrations, and realistic lab exercises.