LLMOps And AIOps Bootcamp With 8 End To End Projects

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

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 Challenges
Understand 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 Management
Set up and manage code repositories with Git & GitHub, integrate pull requests, branching strategies, and project workflows.

CI/CD Pipeline with Jenkins & GitHub Actions
Automate training, testing, and deployment pipelines using Jenkins, GitHub Actions, and custom AWS runners to streamline model delivery.

FastAPI for LLM Deployment
Package and expose LLM services using FastAPI, and deploy inference endpoints with proper error handling, security, and logging.

Groq & HuggingFace Integration
Integrate Groq API for blazing-fast LLM inference. Use HuggingFace models, fine-tuning, and hosting options to deploy custom language models.

Containerization & Quality Checks
Learn 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 Search
Work with vector databases like FAISS, Weaviate, or Pinecone to implement semantic search and Retrieval-Augmented Generation (RAG) pipelines.

Monitoring and Observability
Monitor your LLM systems using Prometheus and Grafana, and ensure system health with logging, alerting, and dashboards.

Kubernetes & Minikube
Orchestrate 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 deployment

• Data Scientists and ML Engineers wanting to productize their LLM solutions

• Backend Developers aiming to master scalable AI deployments

• Anyone interested in the intersection of LLMs, MLOps, DevOps, and Cloud

Technologies 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.