The Lean AI Handbook

A hands-on framework that combines Lean, DataOps, MLOps, and cross functional delivery to ship AI from prototype to production with speed.

AI projects fail more often, not because the math is hard, but because delivery is. The Lean AI Handbook shows leaders, analysts, and AI builders how to build, deploy, and scale AI that delivers real business value, quickly and reliably using Lean and Agile practices. You'll learn how to streamline the path to production with Product Management, DataOps and MLOps work in small batches, focus on the right metrics, and ship models, agents, and insights that keep delivering after launch.

As AI investment grows, many programs still stall at pilot purgatory. The Lean AI Handbook offers a practical People-Process-Tools guide for putting AI into everyday products. Combining Lean and Agile with DataOps and MLOps, it shows you how to remove bottlenecks, build simpler and faster models, and use automation, observability, and feedback loops to scale with confidence. It also covers the last mile, communicating insights clearly, building stakeholder trust, reducing delivery risk, and leading teams that grow with the business.

Key Benefits & Topics Covered:

Overcome the AI delivery bottleneck: Identify the reasons for delayed delivery and necessary changes to increase speed.

Apply Lean thinking and Agile building and shipping AI: Work in small batches, learn fast, keep effort aligned with measurable business outcomes.

Use DataOps as the backbone of Lean AI: Automate pipelines, make data reliable, and deliver AI at scale with modern continuous delivery focused engineering practices.

Master the last mile from modeling to production: Get your models out of prototype theater and deployed to production where they drive real business decisions and customer value.

Lead high-performing AI engineering teams: Scale teams effectively by structuring roles, workflows, and expectations to deliver continuous value to people, processes, and technology.

Ship and monitor models that stay reliable: Use MLOps, observability, and model monitoring to deploy AI safely, catch drift, and minimize blast radius before it erodes customer confidence and damages company brand.