LLMOps-Large Language Model Operations Mastery
LLMOps Certification Course is a practical training program that teaches you how to build, deploy, monitor, and scale Large Language Model (LLM) applications. Learn RAG, LangChain, LangGraph, LoRA/QLoRA, vector databases, AI safety, and production-ready Generative AI workflows.
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This Course Includes:
- On-demand Sessions
- Downloadable Resources
- Portfolio Website
- Live Industry Project
- Freelance Projects
- Lifetime Inxyme Membership
Certificate of Completion
Validate your skills and enhance your resume upon completing all modules.
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“Excellent training center for SAP Material Management certification courses. The sessions were interactive, and the placement support helped me gain confidence for interviews. The practical assignments made learning easy and effective.”
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“Excellent curriculum and amazing support from the team. I landed my first MNC job within two months of completing the program. Thank you Inxyme!”
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Sheeba Jaidiya
Inxyme Verified Learner
“Excellent training center for SAP Material Management certification courses. The sessions were interactive, and the placement support helped me gain confidence for interviews. The practical assignments made learning easy and effective.”
Vaibhav Thakur
Inxyme Verified Learner
“Excellent curriculum and amazing support from the team. I landed my first MNC job within two months of completing the program. Thank you Inxyme!”
Ritika Bhadani
Inxyme Verified Learner
“Myself Ritika I have learned Data Science and Power BI form Inxyme, As students I have experienced well structured training and practical from here. The trainer explained every concept step-by-step clear my all doubts with practical examples and real-time scenario, thats helped me build my knowledge strong and improve my skills in data science”
Amit Verma
Inxyme Verified Learner
“I had a great learning experience with Inxyme while pursuing Data Science. The course was well-structured, practical, and helped me understand important concepts with real-world applications. The guidance and support from the trainers were really helpful throughout the learning journey. Highly recommended for anyone looking to build a career in Data Science!”
Sheeba Jaidiya
Inxyme Verified Learner
“Excellent training center for SAP Material Management certification courses. The sessions were interactive, and the placement support helped me gain confidence for interviews. The practical assignments made learning easy and effective.”
Vaibhav Thakur
Inxyme Verified Learner
“Excellent curriculum and amazing support from the team. I landed my first MNC job within two months of completing the program. Thank you Inxyme!”
Ritika Bhadani
Inxyme Verified Learner
“Myself Ritika I have learned Data Science and Power BI form Inxyme, As students I have experienced well structured training and practical from here. The trainer explained every concept step-by-step clear my all doubts with practical examples and real-time scenario, thats helped me build my knowledge strong and improve my skills in data science”
Amit Verma
Inxyme Verified Learner
“I had a great learning experience with Inxyme while pursuing Data Science. The course was well-structured, practical, and helped me understand important concepts with real-world applications. The guidance and support from the trainers were really helpful throughout the learning journey. Highly recommended for anyone looking to build a career in Data Science!”
Sheeba Jaidiya
Inxyme Verified Learner
“Excellent training center for SAP Material Management certification courses. The sessions were interactive, and the placement support helped me gain confidence for interviews. The practical assignments made learning easy and effective.”
Vaibhav Thakur
Inxyme Verified Learner
“Excellent curriculum and amazing support from the team. I landed my first MNC job within two months of completing the program. Thank you Inxyme!”
Ritika Bhadani
Inxyme Verified Learner
“Myself Ritika I have learned Data Science and Power BI form Inxyme, As students I have experienced well structured training and practical from here. The trainer explained every concept step-by-step clear my all doubts with practical examples and real-time scenario, thats helped me build my knowledge strong and improve my skills in data science”
Amit Verma
Inxyme Verified Learner
“I had a great learning experience with Inxyme while pursuing Data Science. The course was well-structured, practical, and helped me understand important concepts with real-world applications. The guidance and support from the trainers were really helpful throughout the learning journey. Highly recommended for anyone looking to build a career in Data Science!”
Sheeba Jaidiya
Inxyme Verified Learner
About This Course
Anyone can toss together a chatbot using an OpenAI API key over a weekend. But lets be real, very few people are going to be able to keep that exact same system running smoothly, accurately & in a state that's not going to break the bank once you've got 10,000 real users slamming it every day. That big gap between getting a working demo up and running some production-grade AI system, thats exactly what this LLMOps course is here to help you bridge.
This LLM Operations Mastery program is a hands-on, engineering-focused course that shows you how to handle the full lifecycle of a large language model application in production. You'll get hands-on experience with Retrieval Augmented Generation at scale, get familiar with some of the parameter-efficient fine-tuning methods out there, like LoRA ,QLoRA and so on, and build out some automated pipelines using LangChain, LangGraph, LangSmith & Weights & Biases. Along the way you'll also learn about vector database concepts and hybrid retrieval techniques that actually make RAG systems usable on a real enterprise scale.
One of the biggest parts of the LLMOps certification course we call AI Trust. What that means is learning how to spot and squash hallucinations, putting in some safety guardrails, automated evaluation frameworks like RAGAS, and keeping an eye on token costs from quietly eating into your margins. These are pretty much the exact skills that separate a portfolio project from a system that a company can actually rely on.
Whether you're a DevOps engineer looking to get into AI infrastructure, a Data Scientist that wants to own production outcomes instead of handing models off to someone else, or a backend engineer curious about how generative AI operations works, this course gives you a practical, tool-by-tool guide. Its built on the same stack that companies scaling out generative AI features are currently hiring for and reflects exactly how this field is actually maturing - the difference between LLMOps and MLOps, the new cost structures that come with dealing with heavy inference workloads and the operational discipline that's needed to keep LLM apps safe and running smoothly at scale.
If you've been browsing around and wondering what actually sets a production-focused LLMOps course apart from a general MLOps course, the honest answer is the time spent on retrieval, agents, and inference serving - specifically for LLMs rather than classic ML models. Most of the machine learning operations content out there is still assuming a static prediction model sitting behind an API. This course assumes a generative system that talks back, pulls in tools, holds onto conversation state and needs to be constantly re-evaluated because thats more or less what the actual teams are dealing with these days.
Who is this course for?
Full Curriculum
Data Engineering for LLMs: RAG at Scale
Orchestration & AI Agent Design
Observability, Monitoring & Tracing
Deployment & Infrastructure at Scale
Skills Student Will Learn
- LLM Lifecycle Management
- RAG Architecture
- Fine-Tuning (LoRA/QLoRA)
- Vector Databases
- AI Observability & Tracing
- Prompt Engineering for Scale
- LangChain / LangGraph
- Model Evaluation (RAGAS)
- Safety Guardrails
- Python for AI
Topics Student Will Learn
- Set up automated evaluation systems to test the accuracy of your AIs and catch any issues with your models or data before they become a problem.
- Get complete visibility & tracing to sort out the kinks in complex, multi-step AI workflows.
- Deploy large language models using the latest and greatest in inference engines to get the speed and responsiveness you need.
- Keep a close eye on the bottom line, as well as the security and compliance headaches that come with running AIs in production.
- Build and manage RAG pipelines, AI agents and multi-agent workflows that can hold up under real-world traffic conditions.
Requirements
No prerequisites available for this course.
Frequently Asked Questions
MLOps is all about churning out predictive models and deploying them, focusing on metrics like accuracy or F1 score. But LLMOps is in a different ballpark, it's all about generative systems, so you add in some extra tricks like prompt versioning, retrieval pipelines, and keeping an eye on hallucination. On top of that, you still need all the usual MLOps stuff like CI/CD, observability, and governance.
You'll come away from this thing having learned about RAG architecture, fine-tuning models with LORA & QLORA, how to orchestrate an AI agent, make observability and tracing work, and get a production deployment up and running using those fancy modern inference engines. And then there's also the stuff about evaluating, putting in safety guardrails, and cutting down costs so you can keep an eye on your live system from start to finish.
The curriculum for this thing covers LangChain, LangGraph, LangSmith, Weights & Biases, vLLM, TGI, Ollama, FastAPI, BentoML, Modal, and all the way down to AWS SageMaker. Plus, there's also the evaluation tooling that folks actually use like RAGAS. The idea is to give you a picture of the real stack that teams use to run LLM applications in production.
Yeah, RAG at scale is a full module, it covers the whole workflow, hybrid search, and re-ranking. And as for fine-tuning, you'll get to know all about parameter-efficient methods like LoRA and QLoRA.
What you'll really do in this course is learn how to build observability into your multi-step AI chains so you can see exactly what's going on at every step of a conversation or agent workflow. And when you throw in automated evaluation frameworks, you get a repeatable way to catch any drift, regressions, and quality issues before users even notice.
Honestly, no, there's no strict prerequisites. But having basic Python knowledge and a general comfort with APIs will really help you through that deployment and orchestration bit.
Yeah, deployment is a super key part of this course - you'll work with all those inference engines like vLLM, TGI, and Ollama, build APIs with FastAPI, and figure out serverless deployment options through BentoML, Modal, and SageMaker. And you'll also get help with CI/CD pipelines that are specifically designed for LLM applications.
Safety and cost management aren't just some side topics, they run through the whole course. You'll learn how to put in guardrails, evaluate outputs for accuracy and reliability, and really keep an eye on token consumption so costs stay under control as you get more users.
Yeah, the curriculum is designed to meet each background where it is. DevOps engineers can get up to speed on AI infrastructure quickly, Data Scientists can learn how to get their models from notebook to production in one shot - and AI professionals can get the depth they need for senior production-facing roles.
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