Understand how we build successful data products.
Explore the fundamental DNA of what makes us Manifold.
AI and machine learning aren't magic, but rather a set of tools that can enable new capabilities. It's important to start with relevant business strategy and go-to-market questions.
Lean AI is a set of mental models that helps us work together smoothly with our partners, in order to create business value faster when implementing AI in their organizations.
Software product development is often described as a maze—a decision maze of both product-market and technology choices. A 10x engineer makes good judgments in navigating that decision maze.
We try to dial back on the "pixie dust" aspect of AI, and look at our client projects within the context of a more traditional product development spectrum.
Learn about some of the tools we use every day.
A greatly expanded v2.0 of our open-source Orbyter toolkit helps teams streamline ML delivery pipelines, with an emphasis on seamless deployment to production.
The hype around ML makes it easy to forget about more tried and true mathematical modeling methods, but they are complementary tools in a larger toolbox.
This post introduces our open-source Python package for implementing mixed effects random forests. MERFs are great if your model has non-negligible random effects.
At Manifold, we have used Dask extensively to build scalable ML pipelines. In this tutorial, we'll cover the basics of Dask, and using Dask for preprocessing.