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