Deploying LLM Apps: MLOps for AI

About this course

Building a cutting-edge LLM application is only half the battle. Getting it into production reliably, with robust monitoring and version control, is where true value is unlocked. This course dives deep into the specialized MLOps practices essential for the unique demands of LLM deployments. We'll cover everything from setting up CI/CD pipelines tailored for model updates and prompt engineering iterations, to implementing effective monitoring strategies that track not just performance but also output quality and potential drift. Forget generic MLOps; this is about the nitty-gritty of making your LLM app a production-ready success. Key areas include containerization with Docker, orchestration using Kubernetes, and leveraging cloud-native tools on platforms like AWS SageMaker or Azure ML. You’ll learn to manage different versions of your models and prompts, conduct A/B testing on new iterations, and establish rollback procedures for seamless updates. This isn't just theory; we'll work through practical examples and code. By the end of this advanced course, you’ll have the skills to confidently deploy, monitor, and maintain LLM-powered applications at scale, ensuring they meet performance benchmarks and business objectives. Prepare to bridge the gap between development and robust production environments.

Requirements

• Solid understanding of machine learning concepts and the LLM lifecycle.
• Familiarity with Python programming and common ML libraries (e.g., TensorFlow, PyTorch).
• Basic knowledge of cloud platforms (AWS, Azure, or GCP) and containerization (Docker).