AgentofHeart
MyBoerse.bz Pro Member
Llmops And Aiops Bootcamp With 9+ End To End Projects
Published 7/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 24.89 GB | Duration: 29h 15m
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
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