Managing AI and LLM Projects with CRISP-DM

Successfully launching AI and Large Language Model (LLM) projects requires more than just writing code; it demands a structured methodology to align technical solutions with business goals. This course guides you through applying the industry-standard CRISP-DM framework specifically tailored for modern AI initiatives. You will learn how to guide an AI project from its initial business conception through data preparation, modeling, evaluation, and operational monitoring. By reading through detailed case studies like support chatbots, document OCR, and Retrieval-Augmented Generation (RAG) pipelines, you will gain the structured project management skills needed to deliver reliable AI products. What you'll learn: - Understand the six phases of the CRISP-DM framework adapted for LLM and generative AI lifecycles. - Translate business objectives into clear technical AI requirements and KPIs. - Evaluate data readiness for complex tasks like document OCR and RAG system integration. - Design validation strategies and modern evaluation patterns to test LLM outputs. - Configure monitoring and observability workflows to track model performance and drift in production. The course begins with foundational definitions of AI project lifecycles and the core principles of the CRISP-DM methodology. You will then progress step-by-step through each phase of the framework, examining real-world scenarios and testing your understanding with conceptual written exercises. This course is designed for beginners, including aspiring AI project managers, product managers, and developers looking to structure their workflows, with no prior programming experience required. Start reading today to build a repeatable, industry-standard framework for your AI initiatives.