Key LLMOps Principles for Deploying Reliable AI Systems

Without robust operations, even high-performing models can degrade or fail silently in production. LLMOps (Large Language Model Operations) has emerged to tackle this, adapting MLOps principles to the unique challenges of LLM-driven applications so they remain reliable and effective

This intermediate-level video course shows how to apply LLMOps in practice. You'll set up end-to-end pipelines for LLMs, from versioning models and prompts to automating deployments via CI/CD. Learn to implement LLM-specific monitoring and logging so issues don't go unnoticed. Explore patterns like automated evaluation, drift detection, and human feedback loops to maintain model quality. You'll also incorporate guardrails such as output filters and fallbacks to handle LLM pitfalls like hallucinations or inappropriate outputs

By the end, you'll be equipped to take LLM projects from prototype to production with confidence. You'll have the know-how to keep your AI applications observable, secure, and dependable long after deployment. In short, you'll be ready to build AI systems that continue to deliver value reliably in real-world conditions.

To access the supplementary materials, scroll down to the 'Resources' section above the 'Course Outline' and click 'Supplemental Content.' This will either initiate a download or redirect you to GitHub.

What you will learn

• Understand why AI models fail silently without proper operations
• Build monitoring and observability into LLM-based applications
• Apply LLMOps best practices for scalable and reliable deployments
• Set up reproducible pipelines and CI/CD for model updates
• Use tools for logging, evaluation, and drift detection in LLMOps
• Design fallback and human-in-the-loop strategies for LLM failures
• Manage prompt versioning and evaluation to improve model outputs
• Implement guardrails to handle LLM hallucinations and unsafe outputs

Audience

This course is designed for practitioners in machine learning and AI who need to deploy and manage models in production. It targets ML engineers, data scientists, MLOps specialists, and AI developers who already understand basic model development. If you've built ML models or prototypes and now want to ensure they run reliably at scale (particularly applications using large language models), this course is for you. An intermediate level of Python and ML knowledge is expected, as we focus on operational techniques rather than introductory ML concepts.

About the Author

Aurimas Griciūnas: Aurimas Griciunas is a recognized AI expert, LinkedIn Top Voice in AI, and the founder of SwirlAI. He previously served as Chief Product Officer at Neptune.ai where he worked closely with top ML teams to scale infrastructure, evaluation, and LLMOps practices across industries. With over a decade of experience at the intersection of data science, machine learning, and software engineering, Aurimas has led AI initiatives in both startups and enterprise environments. His mission is to bridge the gap between hype and reality by teaching engineers how to build systems that work in the real world.