Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place! Read More
Daily Archives: September 1, 2020
Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.
So you need to redesign your company’s data infrastructure.
Do you buy a solution from a big integration company like IBM, Cloudera, or Amazon? Do you engage many small startups, each focused on one part of the problem? A little of both? We see trends shifting towards focused best-of-breed platforms. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows.
This article, which examines this shift in more depth, is an opinionated result of countless conversations with data scientists about their needs in modern data science workflows. Read More
Google runs largest chemistry calculation on quantum computer
Google has successfully performed the largest chemical simulation on a quantum computer to date, a feat that can unlock new frontiers in chemistry, improving a wide variety of industries.
The Google AI Quantum team used a noise-robust variational quantum eigensolver (VQE) to directly simulate a chemical mechanism via a quantum algorithm. Read More
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