— Insight —
There are enormous opportunities for AI-driven businesses but AI businesses are hard to build efficiently. In this post, Martin Casado and Matt Bornstein from Andreessen Horowitz explore some of the key challenges and how they're being tackled by top teams in industry.
Should you be a generalist or a specialist? Not everyone will agree but Eugene Yan makes a great case in this post that generalists are more effective on data science teams.
— Profiles —
Most people don't realize it but farming has become a data-driven tech industry. In this tour, Chris Padwick walks through the capabilities of the coming generation of smart AG machines and if you're not already familiar with this space, it's impressive!
— Tools and Techniques —
This announcement on the Netflix Tech Blog introduces a new field to watch! Computational Causal Inferences aims to develop software specializing in causal inference that can analyze massive datasets with a variety of causal effects, in a performant, general, and robust way.
Jupyter Book is an open source project for building interactive websites and publication-quality documents using computational content. This new version uses the Sphinx Documentation Engine and offers a souped-up version of Markdown, smarter builds and more output types.
This new initiative aims to develop API standards for n-dimensional arrays and dataframes to help fix software fragmentation.
For anyone who might have an important presentation coming up, this framework by Grace Teoh is next-level. It's not just that her approach is incredibly well organized and thorough, check out her online meeting setup in part 3. Wow.
The Global Master of Management Analytics from Smith School of Business at Queen’s University is a 12-month program that can be taken from anywhere in the world. Master the essential strategies for applying analytics to business needs in this ground-breaking program.