AI Model Development and Maintenance Guide

Building an AI product is only the first step; keeping it accurate, reliable, and efficient in production is where the real challenge begins. This text-based course helps you build a solid understanding of the entire AI model lifecycle, from initial training to long-term maintenance. Through clear explanations and structured self-assessment scenarios, you will transition from a theoretical understanding of machine learning to confidently managing real-world AI model lifecycles. What you'll learn: Understand the core phases of the AI model development lifecycle, from data preparation to training; Evaluate model performance using key metrics and identify common pitfalls like overfitting; Implement model monitoring strategies to detect data drift and concept drift in production; Apply modern MLOps practices for versioning models and automating continuous integration; Analyze maintenance workflows for updating and retraining models safely without service interruption; Explore foundational concepts of LLM evaluation and maintaining generative AI systems. The course starts with fundamental definitions and core terminology before guiding you through development methodologies, deployment strategies, and ongoing maintenance frameworks. This course is designed for beginners, aspiring product managers, and junior developers looking to understand how AI models are managed in production, with no prior programming experience required. Start reading today to build your foundational knowledge of AI product management and maintenance.