Deploying and Maintaining RAG Systems
RAG systems are transforming how enterprises ground LLMs in real data—but deployment and maintenance remain critical bottlenecks. This 27-minute course cuts through the noise to show you exactly how to deploy, monitor, and maintain RAG pipelines in production environments.
AIU.ac Verdict: Essential for ML engineers, platform engineers, and AI architects moving RAG from proof-of-concept to production. You’ll gain hands-on deployment patterns and operational best practices. Note: assumes foundational knowledge of RAG concepts and vector databases.
What This Course Covers
The course covers end-to-end RAG deployment workflows, including infrastructure setup, retrieval pipeline optimisation, and integration with LLM endpoints. You’ll explore containerisation strategies, scaling considerations, and monitoring frameworks that catch retrieval failures before they impact users. Harsh Karna walks through real-world deployment scenarios using industry-standard tools and sandboxes.
Practical modules focus on maintaining RAG systems post-deployment: version control for embeddings, handling stale or corrupted vector stores, performance tuning under load, and observability patterns. You’ll learn cost-optimisation techniques, failover strategies, and how to debug retrieval quality issues when production queries underperform.
Who Is This Course For?
Ideal for:
- ML/Platform Engineers: Building RAG systems for production and need deployment and operational playbooks.
- AI Architects: Designing enterprise RAG infrastructure and responsible for system reliability and scaling.
- DevOps/SRE Teams: Managing generative AI workloads and needing RAG-specific monitoring and maintenance strategies.
May not suit:
- RAG Beginners: This assumes you understand retrieval-augmented generation fundamentals; start with RAG architecture courses first.
- Data Scientists Focused on Model Training: Course emphasises operations and deployment, not fine-tuning or prompt engineering.
Frequently Asked Questions
How long does Deploying and Maintaining RAG Systems take?
27 minutes of video content. Most learners complete it in one focused session, though hands-on lab exercises may extend your total time investment.
What prerequisites do I need?
You should understand RAG concepts, vector databases, and basic containerisation (Docker). Familiarity with Python and cloud platforms (AWS/Azure/GCP) is helpful.
Does this course cover specific tools or frameworks?
Yes—Harsh Karna uses industry-standard tools and sandboxes for practical demonstrations. The patterns taught apply across LangChain, LlamaIndex, and custom RAG implementations.
Will I get hands-on lab access?
Yes. Pluralsight includes interactive labs and sandboxes where you can deploy and maintain RAG systems in a safe environment.
Course by Harsh Karna on Pluralsight. Duration: 0h 27m. Last verified by AIU.ac: March 2026.
