From model training to real-world deployment, this chapter explains the critical steps that turn machine learning into a business-ready system. Learn how organizations finalize models, deploy with A/B testing, monitor drift, and continuously improve AI in production.

This chapter explains how machine learning models are trained, tested, improved, and selected, while avoiding overfitting, underfitting, and model drift, to ensure they deliver real business value.

AI tools optimize individual tasks, but business outcomes are constrained by how work flows across people, systems, and tools. Workflow intelligence turns fragmented AI activity into measurable outcomes by orchestrating the edges where real value is created.