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.