“The single biggest mistake in AI is not the model—it’s the refusal to walk away when the strategy is broken.” – Thomas H. Davenport, author of Competing on Analytics When Machines Learn and Leaders Don’t: Why AI Projects Fail and […]
The Sunk Cost of Intelligence: Lessons from AI’s Failures and False Starts Read More »
“The biggest risk of software architecture is over-engineering: building for scale or flexibility you may never need.” — Martin Fowler Architecting for Evolution: Building Systems That Outlast the Moment In technology, nothing is permanent except change. Today’s cutting-edge architectures become
Future-Proof or Fool’s Errand? How to Architect for Evolution Without Over-Engineering Read More »
“AI models don’t fail in sudden crashes—they fail in slow drifts. The best teams aren’t the ones who never drift, but the ones who notice early and correct often.” — Inspired by Andrew Ng’s advocacy for data-centric AI When Models
When Models Wander: Managing Drift in ML and LLM Systems Read More »
“In AI, as in computing history, the real battles aren’t won with flashy demos but with the standards and protocols that quietly make systems interoperable and trustworthy.” — Anonymous industry maxim, paraphrasing lessons from the early internet era Beyond the
From Fine-Tuning to Protocol Wars: Building the LLM Tech Stack That Lasts Read More »
“Data is not just the raw material of intelligence—it is the context, the memory, and the connective tissue that makes learning possible.” — Andrew Ng From Labels to Vectors: Building the Data Foundations of Modern AI In the early days
The Secret Life of Data: How Labels, Embeddings, and Vector DBs Power Intelligence Read More »
“Every system is perfectly designed to get the results it gets. If your AI produces chaos when attacked, it’s because you designed it that way.” — Adapted from W. Edwards Deming The New Perimeter: Securing Large Language Models in the
When Prompts Attack: Building Defensible LLM Infrastructure Read More »
“Data becomes an asset not when it is collected, but when it is cared for, trusted, and delivered with purpose.” — Zhamak Dehghani Data as a Product: Turning Raw Information into Value In the early 2000s, companies thought of data
Data as a Product: Building Assets, Not Just Pipelines Read More »
“Data doesn’t become valuable when it’s collected—it becomes valuable when it’s connected, trusted, and scaled responsibly.” – Adapted from modern data governance principles Weaving the Future: Data Fabric vs. Data Mesh in Modern Enterprises The enterprise data landscape has always
From Swamps to Systems: How Data Fabric and Mesh Reimagine Trust and Scale Read More »
“Effective data governance is not about rules for the sake of compliance; it is about enabling reliable, repeatable, and scalable decision-making.” — Adapted from John Ladley The Data Governance Cookbook A Practical Guide to Building a Scalable, Trustworthy Data Program
Governance That Works: A Practical Cookbook for Modern Data Leaders Read More »
“Good governance is not about control. It’s about creating the conditions where trust, accountability, and value emerge naturally from the way people already work.” — Bob Seiner, Non-Invasive Data Governance From Chaos to Clarity: Evolving Data Governance Without Killing Innovation
Non-Invasive to Federated: A Roadmap for Governance That Scales with Your Business Read More »
