Putting machine learning into production requires a fundamentally different approach than it did even five years ago, due to expanded capabilities and greater complexity. Leaders managing data science and AI teams need to understand what’s feasibly achievable with modern ML and LLM systems, the inevitable challenges teams will face, and how to navigate the ecosystem to provide effective support. This course bridges the gap between traditional MLOps and the emerging world of LLMOps, covering everything from data versioning and experiment tracking to retrieval-augmented generation and AI agents. Discover how to make critical architectural decisions—when to use third-party APIs versus self-hosted models, when RAG is appropriate versus fine-tuning, and how to build safe, scalable AI systems with proper monitoring and governance. By the end of this course, you’ll have a modern mental model of AI delivery, be ready to leverage the tools and infrastructure your teams need, and know how to assess skills, upskill existing talent, and create an environment where AI innovation can thrive. This course prepares leaders not just to manage AI projects, but to strategically position organizations for success in an AI-driven future.
Learn More
