Forging a strong link between better models and better products scales the business value generated from data and results in a deep AI moat. Read the full story on Medium.
Data strategy is about building better products, not accumulating more data or using fancier methods. Product/data fit optimizes how your product uses data to create value. Read the full story on Medium.
In a successful machine learning product, business goals drive modeling goals. To do that, you have to adapt the concept of acceptance criteria to data science tasks. Read the full story on Medium.
A few weeks ago, I spoke at the AI Leaders Summit in Boston about product/data fit. At the conference, I had the pleasure of being interviewed by Gregg Stebben of Forbes Books Radio. We talked about product/data fit: data strategy, AI strategy, and how organizations can successfully use AI to accelerate their business.
Check out the interview here!
How to accelerate your startup, build data equity, control data debt, and get value from your data before you hire your first data scientist: Read the full story on Medium.
Machine learning and AI add new challenges to each step of the Lean Startup's build-measure-learn cycle. Understanding them is key to building great AI products. Read my full post on Medium.