LLMOps in Action: Build, Deploy & Scale RAG-Powered AI Systems
IntermediateLevel
138+Students Enrolled
2 Hrs Duration

About this Course
- Learn how to build, deploy, and scale RAG-powered AI systems using LLMOps for advanced AI applications.
- Master LLMOps principles for deploying scalable, robust AI systems, with a focus on RAG architecture and optimization.
- Explore deployment best practices, from architecture design to monitoring, for building production-ready AI systems with LLMOps.
- Gain hands-on experience building RAG-based AI systems with LLMOps, including scaling, deployment, and evaluation strategies.
Course Benefits
- Build and deploy production-ready RAG AI systems using LLMOps best practices and modern tools.
- Gain hands-on experience in monitoring, evaluation, and improving AI system performance at scale.
- Learn to design scalable AI architectures with strong guardrails, governance, and reliability.
- Develop practical skills to manage end-to-end LLM lifecycle from build to deployment and scaling.
Learning Outcomes
Build RAG Systems
Build scalable RAG-powered AI systems with LLMOps.
Deploy AI Systems
Deploy robust, production-ready AI systems with LLMOps.
Scale AI Solutions
Scale RAG-based AI systems for real-world performance.
Who Should Enroll
- AI developers looking to master the deployment, scaling, and reliability of RAG-powered AI systems.
- Data engineers and architects interested in optimizing AI workflows, pipelines, and operations with LLMOps.
- AI enthusiasts eager to learn cutting-edge techniques for designing and building scalable AI solutions.
Course Curriculum
Learn LLMOps through a structured journey covering RAG architecture, system design, deployment pipelines, and monitoring. Build and scale production-ready AI systems with hands-on projects and real-world workflows.
Build a strong foundation in LLMOps by understanding its purpose, lifecycle, and core components. Explore roles, system design, and compare hosted vs self-hosted LLM models.
1. What is LLMOps?
2. Why LLMOps Exists
3. LLM Application Lifecycle
4. Components of the LLMOps Stack
5. LLMOps Roles and Responsibilities
6. Hosted v/s Self Hosted LLM Models
Understand real-world AI challenges and dive into LLM architecture. Compare hosted APIs with open-source models like vLLM to choose the right setup for performance, cost, and scalability.
1. Real-World Problem Statement
2. Deep Dive into LLM Architecture
3. Hosted API Models vs Open-Source Models (vLLM)
Design and build production-ready LLM systems using config-driven workflows, multi-LLM integration, guardrails, and versioned prompts. Implement end-to-end RAG pipelines with evaluation and reliability layers.
1. Introduction
2. Environment Setup & Production Folder Structure
3. Config-Driven LLM & Embedding Management
4. Reliability, Guardrails & Governance Layer
5. Multi-LLM Integration & Provider Abstraction
6. Prompt Engineering with Version Control
7. End-to-End RAG Pipeline
8. Evaluation Layer: Structured Output & LLMOps Signals
Apply LLMOps in real-world scenarios by improving reliability with retrieval grounding, exploring runtime patterns, scaling strategies, and deploying AI systems on the cloud.
1. Hands On: Improve Reliability with Retrieval Grounding
2. Hands On: LLM Runtime patterns
3. Scaling Concepts
4. Cloud Deployment
Learn how to monitor LLM systems, evaluate answer quality over time, and implement guardrails. Understand drift, governance, and feedback loops to continuously improve production AI systems.
1. Monitoring Essentials
2. Evaluation - Measuring Answer Quality Over Time
3. Guardrails & Feedback Loop
4. Operational Signals - Drift, Governance, and Improvement
Get this Course Now
With this course you’ll get
- 2 Hours
Duration
- 4.8
Average Rating
- Intermediate
Level
Certificate of completion
Earn a professional certificate upon course completion
- Career Advancement Credential
- Industry-Recognized Credential
- Shareable Achievement

Frequently Asked Questions
Looking for answers to other questions?
LLMOps refers to the practices required to build, deploy, monitor, and scale Large Language Model applications in production. It is important because building AI systems is not enough ensuring reliability, performance, cost control, and governance is critical for real-world adoption.
Basic understanding of AI concepts is helpful, but not mandatory. The course is structured to gradually build your knowledge from fundamentals to advanced topics like RAG systems, deployment, and monitoring, making it suitable for both beginners and intermediate learners.
You will work with modern LLMOps tools such as vector databases, APIs, deployment frameworks, monitoring systems, and evaluation pipelines. The course focuses on practical implementation rather than theory, ensuring you gain hands-on experience with real-world tools.
Yes, this course is highly hands-on. You will build end-to-end RAG-powered AI systems, including designing architecture, integrating LLMs, deploying applications, and adding monitoring and evaluation layers to simulate real production environments.
Retrieval-Augmented Generation (RAG) systems combine language models with external knowledge sources like databases or documents. This approach improves accuracy, enables up-to-date responses, and makes AI systems more reliable for business use cases.
Yes, deployment is a key focus area. You will learn how to move from prototype to production, including API deployment, system architecture, runtime considerations, and scaling strategies for handling real-world usage.
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