Jenkins CI/CD, Docker, K8s, AWS/GCP, Prometheus monitoring & vector DBs for production LLM deployment with real projects

What you'll learn

Build and deploy real-world AI apps using Langchain, FAISS, ChromaDB, and other cutting-edge tools.

Set up CI/CD pipelines using Jenkins, GitHub Actions, CircleCI, GitLab, and ArgoCD.

Use Docker, Kubernetes, AWS, and GCP to deploy and scale AI applications.

Monitor and secure AI systems using Trivy, Prometheus, Grafana, and the ELK Stack

Requirements

Modular Python Programming Knowledge

Basic Generative AI like Langchain,Vector Databases,etc

Description

Are you ready to take your Generative AI and LLM (Large Language Model) skills to a production-ready level? This comprehensive hands-on course on LLMOps is designed for developers, data scientists, MLOps engineers, and AI enthusiasts who want to build, manage, and deploy scalable LLM applications using cutting-edge tools and modern cloud-native technologies.In this course, you will learn how to bridge the gap between building powerful LLM applications and deploying them in real-world production environments using GitHub, Jenkins, Docker, Kubernetes, FastAPI, Cloud Services (AWS & GCP), and CI/CD pipelines.We will walk through multiple end-to-end projects that demonstrate how to operationalize HuggingFace Transformers, fine-tuned models, and Groq API deployments with performance monitoring using Prometheus, Grafana, and SonarQube. You'll also learn how to manage infrastructure and orchestration using Kubernetes (Minikube, GKE), AWS Fargate, and Google Artifact Registry (GAR).What You Will Learn:Introduction to LLMOps & Production ChallengesUnderstand the challenges of deploying LLMs and how MLOps principles extend to LLMOps. Learn best practices for scaling and maintaining these models efficiently.Version Control & Source ManagementSet up and manage code repositories with Git & GitHub, integrate pull requests, branching strategies, and project workflows.CI/CD Pipeline with Jenkins & GitHub ActionsAutomate training, testing, and deployment pipelines using Jenkins, GitHub Actions, and custom AWS runners to streamline model delivery.FastAPI for LLM DeploymentPackage and expose LLM services using FastAPI, and deploy inference endpoints with proper error handling, security, and logging.Groq & HuggingFace IntegrationIntegrate Groq API for blazing-fast LLM inference. Use HuggingFace models, fine-tuning, and hosting options to deploy custom language models.Containerization & Quality ChecksLearn how to containerize your LLM applications using Docker. Ensure code quality and maintainability using SonarQube and other static analysis tools.Cloud-Native Deployments (AWS & GCP)Deploy applications using AWS Fargate, GCP GKE, and integrate with GAR (Google Artifact Registry). Learn how to manage secrets, storage, and scalability.Vector Databases & Semantic SearchWork with vector databases like FAISS, Weaviate, or Pinecone to implement semantic search and Retrieval-Augmented Generation (RAG) pipelines.Monitoring and ObservabilityMonitor your LLM systems using Prometheus and Grafana, and ensure system health with logging, alerting, and dashboards.Kubernetes & MinikubeOrchestrate containers and scale LLM workloads using Kubernetes, both locally with Minikube and on the cloud using GKE (Google Kubernetes Engine).Who Should Enroll?MLOps and DevOps Engineers looking to break into LLM deploymentData Scientists and ML Engineers wanting to productize their LLM solutionsBackend Developers aiming to master scalable AI deploymentsAnyone interested in the intersection of LLMs, MLOps, DevOps, and CloudTechnologies Covered:Git, GitHub, Jenkins, Docker, FastAPI, Groq, HuggingFace, SonarQube, AWS Fargate, AWS Runner, GCP, Google Kubernetes Engine (GKE), Google Artifact Registry (GAR), Minikube, Vector Databases, Prometheus, Grafana, Kubernetes, and more.By the end of this course, you'll have hands-on experience deploying, monitoring, and scaling LLM applications with production-grade infrastructure, giving you a competitive edge in building real-world AI systems.Get ready to level up your LLMOps journey! Enroll now and build the future of Generative AI.

Overview

Section 1: COURSE INTRODUCTION

Lecture 1 Introduction to the Course

Section 2: Medical RAG Chatbot using Jenkins,Trivy,AWS,FAISS,Langchain,Flask,HTML/CSS

Lecture 2 Introduction to the Project

Lecture 3 Project & API Setup ( HuggingFace )

Lecture 4 Configuration Code

Lecture 5 PDF Loader Code

Lecture 6 Embeddings Code

Lecture 7 Vector Store Code using FAISS

Lecture 8 Data Loader Code

Lecture 9 LLM Setup Code

Lecture 10 Retriever Code

Lecture 11 Main Application using Flask & HTML

Lecture 12 Code Versioning & Dockerfile

Lecture 13 Jenkins Setup for CI-CD Deployment

Lecture 14 GitHub Integration with Jenkins

Lecture 15 Build, Scan with AquaTrivy & Push to AWS ECR

Lecture 16 Deployment to AWS Runner

Lecture 17 Cleanup Process

Section 3: Multi AI Agent using,Jenkins,SonarQube,FastAPI,Langchain,Langgraph,AWS ECS

Lecture 18 Introduction to the Project

Lecture 19 Project and API Setup ( Groq & Tavily )

Lecture 20 Configuration Code

Lecture 21 Core Code

Lecture 22 Backend using FastAPI

Lecture 23 Frontend using Streamlit

Lecture 24 Main Application Code

Lecture 25 Code Versioning

Lecture 26 Dockerfile

Lecture 27 Jenkins Setup for CI-CD Deployment

Lecture 28 GitHub Integration with Jenkins

Lecture 29 SonarQube Integration with Jenkins

Lecture 30 Build & Push Image to AWS ECR

Lecture 31 Deployment to AWS Fargate

Lecture 32 Cleanup Process

Section 4: AI Anime Recommender using Grafana Cloud,Minikube,ChromaDB,Langchain

Lecture 33 Introduction to the Project

Lecture 34 Project and API Setup ( Groq and HuggingFace )

Lecture 35 Configuration Code

Lecture 36 Data Loader Class Code

Lecture 37 Vector Store Code using Chroma

Lecture 38 Prompt Templates Code

Lecture 39 Recommender Class Code

Lecture 40 Training and Recommendation Pipeline

Lecture 41 Main Application Code

Lecture 42 Dockerfile , Kubernetes Deployment File and Code Versioning

Lecture 43 GCP VM Instance Setup with Docker Engine , Minikube and Kubectl

Lecture 44 GitHub Integration with Local and VM

Lecture 45 GCP Firewall Rule Setup

Lecture 46 Deployment of App on the Kubernetes

Lecture 47 Monitoring Kubernetes using Grafana Cloud

Lecture 48 Cleanup Process

Section 5: Flipkart Product Recommender using Prometheus,Grafana,Minikube,AstraDB,Langchain

Lecture 49 Introduction to the Project

Lecture 50 Project and API Setup ( Groq , HuggingFace and AstraDB )

Lecture 51 Configuration Code

Lecture 52 Data Converter Code

Lecture 53 Data Ingestion Code

Lecture 54 RAG Pipeline with Memory Code

Lecture 55 Main Application Code using Flask , HTML/CSS

Lecture 56 Dockerfile and Kubernetes Deployment File Code

Lecture 57 Prometheus Deployment File Code

Lecture 58 Grafana Deployment File Code

Lecture 59 Code Versioning using GitHub

Lecture 60 GCP VM Instance Setup with Docker Engine,Minikube,Kubectl

Lecture 61 GitHub Integration with VM

Lecture 62 GCP Firewall Rule Setup

Lecture 63 Build and Deploy Application on Kubernetes

Lecture 64 Monitor Application using Prometheus and Grafana

Section 6: AI Travel Planner using Filebeat,ELK(ElasticSearch,Logstash,Kibana) , Kubernetes

Lecture 65 Introduction to the Project

Lecture 66 Project and API Setup ( Groq )

Lecture 67 Configuration Code

Lecture 68 Itinerary Chain Code

Lecture 69 Core Planner Code

Lecture 70 Main Application Code using Streamlit

Lecture 71 Dockerfile, Kubernetes Deployment File and Code Versioning using GitHub

Lecture 72 Filebeat Deployment Code

Lecture 73 Logstash Deployment Code

Lecture 74 ElasticSearch Deployment Code

Lecture 75 Kibana Deployment Code

Lecture 76 GCP VM Instance Setup with Docker Engine,Minikube,Kubectl

Lecture 77 GitHub Integration with VM

Lecture 78 GCP Firewall Rule Setup

Lecture 79 Deploy your Application on Kubernetes

Lecture 80 Logging Management using ELK Stack with Filebeat

Section 7: Study Buddy AI using Minikube,Jenkins,ArgoCD,GitOps,Langchain,DockerHub

Lecture 81 Introduction to the Project

Lecture 82 Project and API Setup ( Groq )

Lecture 83 Configuration Code

Lecture 84 Question Schemas Models Code

Lecture 85 Prompt Templates Code

Lecture 86 GROQ Client Setup Code

Lecture 87 Question Generator Code

Lecture 88 Helper Class Code for Application

Lecture 89 Main Application Code

Lecture 90 Code Versioning and Dockerfile

Lecture 91 Kubernetes Manifests Files Code

Lecture 92 GCP VM Instance Setup for Docker,Minikube,Kubectl

Lecture 93 Jenkins Setup for Continuous Integration ( CI )

Lecture 94 GitHub Integration with Jenkins

Lecture 95 Build and Push Docker Image to DockerHub

Lecture 96 ArgoCD Setup for Deployment - Part 1

Lecture 97 ArgoCD Setup for Deployment - Part 2

Lecture 98 ArgoCD Setup for Deployment - Part 3

Lecture 99 WebHooks , Some Stages and Cleanup

Section 8: Celebrity Detector & QA using Kubernetes,CircleCI,Groq,Llama-4,OpenCV ,Flask

Lecture 100 Introduction to the Project

Lecture 101 Project and API Setup ( Groq )

Lecture 102 Image Handler Code with OpenCV

Lecture 103 Celebrity Detector Code using Llama-4

Lecture 104 Question Answer Engine Code

Lecture 105 Flask Backend Routes Code

Lecture 106 Main Application Code using HTML/CSS and Flask

Lecture 107 Dockerfile , Kubernetes Deployment File and Code Versioning using GitHub

Lecture 108 GCP Setup ( Service Accounts , GKE, GAR )

Lecture 109 Circle CI Pipeline Code

Lecture 110 Full CI/CD Deployment of Application on GKE

Section 9: AI Music Composer using GitLab CI/CD,GCP Kubernetes, Music21, Synthesizer,

Lecture 111 Introduction to the Project

Lecture 112 Project and API Setup ( Groq )

Lecture 113 Utility Functions Code

Lecture 114 Core Code for Application

Lecture 115 Main Application Code using Streamlit

Lecture 116 Dockerfile and Kubernetes Deployment File

Lecture 117 Code Versioning using GitLab

Lecture 118 GCP Setup ( Service Accounts , GKE, GAR )

Lecture 119 GitLab CI/CD Code

Lecture 120 Full CI-CD Deployment to GKE

Students or professionals aiming to enter the AI + DevOps job market