Machine learning test metrics should always be calculated with credible intervals. Credible intervals give you upper and lower bounds on test performance so you know how big your test needs to be and when to trust your models. Humanloop Active Testing can give you uncertainty bounds on your test metrics and makes this easy.

Human-in-the-loop AI is an automation approach which means you can quickly deploy a working model, with less data and with guaranteed quality predictions. In this post we'll explain what HITL is and how you can make use of this approach in your own AI projects.

Machine learning systems are born through the marriage of both code and data. Academia mostly focuses on ways to improve the learning algorithms, the how of machine learning. When you come to build practical AI systems though the dataset you're training on has at least as much impact on performance as the choice of algorithm.

We discuss the real world performance of Open AI's GPT-3 language model and speculate on what the future of large pre-trained language models may hold.

The most surprising lessons about how to build tooling for machine learning, what is needed going forwards and why domain experts will play a much larger role in the future of AI.