LLMOps Foundations: Building Production Pipelines for Language Models

Moving a large language model from a local prototype to a reliable production environment requires specialized operational strategies. This course teaches you how to bridge the gap between AI development and robust system operations using LLMOps. You will gain the skills to establish automated evaluation pipelines, monitor performance metrics like cost and latency, and secure your applications against common vulnerabilities. What you'll learn: - Understand core LLMOps concepts, including prompt versioning and model lifecycle management. - Configure automated evaluation frameworks using tools like LangSmith and Ragas to measure output quality. - Integrate quality gates into your CI/CD pipelines to prevent regression in model responses. - Monitor production metrics such as latency, token usage, cost, and drift with real-time alerting. - Apply security best practices to protect LLM applications from prompt injection and data leaks. - Generate synthetic test data and implement LLM-based judges for automated quality assurance. The course begins with foundational LLM architecture and operational terminology before guiding you through practical configuration steps. You will read detailed explanations and study real-world configuration scripts to build your own production-ready pipeline. This course is designed for software engineers, data scientists, and DevOps beginners who want to transition into LLM operations, with no prior LLMOps experience required. Start building reliable, secure, and cost-effective LLM systems today.