A colleague and I have just released on arXiv a paper titled “Enabling Secure and Ephemeral AI Workloads in Data Mesh Environments”. The key innovation is in pushing the now well-established idea of minimal immutable data structures up and down the software infrastructure stack a bit further than what others have done, resulting in a … More Secure and Ephemeral AI Workloads in Data Mesh Environments
Slightly less than 5 years ago, I had the fortune of being hauled in front of the then richest man in Asia and asked to talk about data science. That was the very first time I had met a billionaire in person so it was kind of exciting. Two things happened: I gave my worst … More Agile Data Science: The Point of It All
The life of a sea squirt has an important lesson for data science. For those who haven’t heard, sea squirts come to life as larvae that swim freely around. In that state, however, they are not capable of feeding so they will soon settle to the bottom of the ocean and cement themselves headfirst to … More Agile Data Science: Start with An Action
Sometimes I can’t help but wonder whether data science as a discipline is still in the dark ages. From my vantage point as a practicing data scientist, I see that too many projects still rely on personal heroics for success. By and large, ours is still a discipline dominated by the need for stars and … More Agile Data Science: Applying Kanban in the Analytics Life Cycle
A colleague recently came back from his MBA class with a useful insight: Agile practices work like a (financial) leverage in project management, magnifying good things on the way up and bad things on the way down. There are already many good books and articles on the benefits of agile software engineering and agile data science so … More Agile Data Science – A Prerequisite
Mental Models 4 Life 