LLMOps Foundations: Building with Agents and Multimodal Models

As Large Language Models evolve, deploying them into production requires more than just basic API calls. Understanding how to manage autonomous agents and handle diverse data types like text, images, and audio is becoming the standard for modern software engineering. This text-based course guides you through the core concepts of LLMOps, focusing on the next generation of AI systems. You will gain a solid conceptual foundation in orchestrating multi-agent workflows, managing multimodal inputs, and optimizing production applications for cost, speed, and reliability. What you'll learn: - Understand the core lifecycle of LLMOps, from model selection and fine-tuning to deployment and monitoring. - Explore agentic architectures and learn how to design autonomous systems that can use tools and make decisions. - Analyze multimodal systems to process and integrate text, image, and audio inputs seamlessly. - Apply modern optimization techniques, including quantization, caching, and structured outputs, to improve model efficiency. - Implement Retrieval-Augmented Generation (RAG) patterns and vector databases to ground your models in custom data. - Evaluate LLM application performance and security using modern testing frameworks and safety guardrails. You will start with foundational LLMOps definitions and terminology before moving step-by-step through agent design, multimodal integration, and production-grade optimization strategies. This course is designed for software developers, data enthusiasts, and technical product managers who want to understand the future of AI operations, with no advanced machine learning background required. Start reading today to build smarter, more efficient AI applications.