Buy or build ML solutions

This article will look at one of the most complex decisions for most organizations starting new AI projects. Should they buy or build the software …

How to organize ML teams

How do you best structure ML teams? In this article, we break down different approaches and go through the pros and cons of these approaches. …

Pricing a vision AI project part 1 : What projects are worth spending money on?

This article, the first of a four-part series, goes through the initial steps needed when getting started with AI. Specifically, we look at how to …

Pricing a vision AI project part 2: Understanding the costs

In our previous article in the series, we looked at how you can prioritize AI projects and gave a quick back-of-the-napkin calculation to use for …

The modern AI workforce part 2: In-house or outsource?

A walkthrough on the costs, benefits, and risks of outsourcing annotation work. We also give you our recommended approach.

The modern AI workforce part 1: Why you need expert annotators

Going through the need for expert annotators and how you can minimize the cost of having expensive experts annotating for you.

Infrastructure that is never noticed

Practical machine learning is about much more than statistics and model architectures only. Resilient and reliable infrastructure is the foundation …

We need "Agile" in machine learning

It’s time for a non-linear, iterative approach that builds highly performant vision AI applications.

Machine learning IRL

Everyone is talking about applying ML to real-world use cases, but there’s very little sharing between teams actually doing the implementation. We …