Learn LLMOps from scratch and build, deploy, and scale RAG-powered AI systems. This playlist covers LLM lifecycle, architecture, deployment, monitoring, evaluation, and guardrails. Through hands-on projects, you’ll gain practical experience in building production-ready AI applications using modern tools and frameworks. Ideal for AI engineers, data scientists, and developers looking to move from prototypes to real-world AI systems.
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LLM Ops Foundation: Building & Scaling Generative AI Systems in Production
In this video, we dive deep into the foundations of LLM Ops (Large Language Model Operations). While MLOps revolutionize…
1
Why LLM Ops Exists: Managing Complexity in Generative AI Systems
In this video, we break down the 5 core reasons why LLM Ops is essential:
1. Infrastructure & Scaling: Why LLMs require…
2
LLM Ops Life Cycle: How to Deploy, Monitor, and Maintain AI Systems
In this video, we explore the LLM Application Life Cycle, a structured framework designed to move your Generative AI pro…
3
LLM Ops Infrastructure: Model Serving, RAG Pipelines, and Observability
In this video, we break down the LLM Ops Stack the full ecosystem of components required to move a Large Language Model …
4
Roles in LLM Ops: Engineering, Data, Governance, and Product
An LLM system is only as good as the team behind it.
While technology is at the heart of LLM Ops, the success of a produ…
5
Hosted LLM APIs vs. Self-Hosting: The Ultimate LLM Ops Decision Guide
In this video, we break down:
Hosted LLM Models: The pros and cons of using providers like OpenAI (GPT), Google (Gemini…
6
What is RAG? A Guide to Retrieval Augmented Generation and RAG Ops
In this video, we dive into how Retrieval Augmented Generation (RAG) solves this problem by grounding AI responses in re…
7
How Production AI Works: A Step-by-Step Architectural Guide
What does a real-world LLM application look like under the hood?
Building a production-ready AI system is about much mor…
8
Hosted APIs vs. Open Source LLMs: Choosing Your RAGOps Strategy
In this video, we cover:
1. Hosted APIs (OpenAI/Gemini): Why they are the fastest way to prototype and how they handle …
9
Building Production AI: Using RAG to Master LLM Ops Foundations
Why are we using a RAG (Retrieval Augmented Generation) system to learn LLM Ops?
In this video, we explain why RAG is m…
10
Hands-on LLM Ops: Setting Up Your Python Dev Environment and Project Structure
In this video, we begin building our LLM Ops project by setting up a professional development environment. Before we wri…
11
RAG Architecture: Managing LLM Providers and Vector Stores via Config
Why should you never hardcode your LLM provider in a production system?
In this video, we explore the power of Config-D…
12
How to Implement Retrieval Guardrails in your RAG Pipeline
How do you ensure your LLM system is reliable and auditable in a production environment?
In this video, we move beyond …
13
How to Integrate Multiple LLMs into One System (OpenAI, Google Gemini, vLLM, Ollama)
How do you build an AI system that isn't locked into a single provider?
In this video, we dive into the core implementat…
14
LLM Ops Tutorial: Prompt Engineering, Versioning, and Dynamic Generation
In a production LLM system, a prompt is not just a string—it is a versioned artifact.
If you want to build reliable AI, …
15
LLM Ops Architecture: Implementing Output Validation and Structured AI Responses
How does a production-grade LLM request actually flow from start to finish?
In this video, we pull everything together a…
16
RAG Evaluation Deep Dive: Measuring AI Quality in Production LLM Ops
How do you know if your RAG system is actually performing well?
In traditional machine learning, we rely on simple accu…
17
LLM Ops: Local FastAPI Deployment, Ollama, and Metadata Analysis
Building a RAG system is only the first step. The real challenge is operating it reliably.
In this video, we move from …
18
LLM Ops: Config-Driven Deployment and Self-Hosted vs. Hosted Runtimes
What happens when you need to switch your LLM provider in production?
In this session of our LLM Ops series, we move bey…
19
LLM Ops: Scaling Large Language Models on Cloud Infrastructure (Azure & FastAPI)
How do you move from a local prototype to a system that handles thousands of users?
The real challenge for any AI applic…
20
How to Deploy LLM Applications: Azure OpenAI, FastAPI, and App Service Scaling
Stop building demos and start deploying production-ready AI.
In this video, we move beyond localhost and take our RAG (…
21
LLM Ops: Monitoring Retrieval, Generation, and User Experience Signals
An LLM application rarely crashes—instead, it degrades slowly.
In production, your AI might look healthy on the outside,…
22
LLM Ops: Building a Quality Gate for Retrieval & Generation (Regression Detection)
The hardest part of AI production isn't a crash—it's a quiet decline in quality.
In this video, we explore why evaluatio…
23
AI Guardrails for RAG Systems: Preventing Hallucinations and Prompt Injections
Your LLM will answer anything you allow it to—unless you stop it.
In a production environment, guardrails aren't just a …
24
Mastering LLM Ops: Tracing, Data Lineage, and the Fine-Tuning Decision
Your LLM system won't crash; it will just get worse.
In this final session of our LLM Ops Foundation course, we tackle …
25
Final Step: Take the Quiz & Claim Your Certificate
Complete your certification step by attempting the quiz here: https://courses.analyticsvidhya.com/courses/llmops-in-acti…
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