PinnedData as a Product vs. Data as a ServiceThe difference between providing “data” and providing “insights” (actually though)Jul 14, 20194Jul 14, 20194
How Data Scientists Can Work Better With EngineersLearning the right skills, owning investigative work, and collaborating on abstractions can go a long wayFeb 3, 20191Feb 3, 20191
Published inHeartbeatMachine Learning models on the edge: mobile and IoTEdge devices are becoming increasingly important—and here’s how machine learning works with themJun 20, 2018Jun 20, 2018
Published inTowards Data ScienceThe missing part of the Machine Learning revolutionDespite the widespread adoption of AI, scaling and deploying AI-based products is as hard as ever; but some new technology is looking to…Nov 15, 20172Nov 15, 20172
Published inZodiac MetricsThe era of the customer-centric businessBusinesses have typically been built around understanding forces, groups, and trends. New technology is changing that paradigm by giving…Nov 7, 2017Nov 7, 2017
Published inDatalogueThe abstracted future of data engineeringThe way we view data and data sources has more or less stayed the same over time, but new technologies suggest a step innovation is…Oct 24, 2017Oct 24, 2017
Machine Learning Abstraction And The Age of AI EaseNew tools are making it easier than ever to get working AI up and runningSep 13, 20177Sep 13, 20177
Published inStartups & Venture CapitalMaking Sense of The Different Types of AI CompaniesA framework for understanding how companies use AISep 6, 20173Sep 6, 20173
How different stages of investors align with foundersEarlier investors may naturally more in-sync with their founders than their later stage counterpartsFeb 7, 2017Feb 7, 2017