It’s hard to stay current and maintain competency in deep learning. It’s a young and fast growing field, which means that groundbreaking research and innovations are coming out really rapidly. But at Masterful, we don’t have a choice: we have to stay current because the promise we make to developers is that our platform automatically delivers state-of-the-art approaches for computer vision models (CV) in a robust and scalable way.
Our mission at Masterful AI is to bring the power and efficiency of modern software development to machine learning. One of the most archaic and error-prone aspects of ML development is getting accurately labeled training data. Through our work with many other ML engineers, we've seen a common fear: no one really knows if simply throwing more labeled training data at their model is going to deliver the accuracy they need. This has big implications, since labeling is slow and expensive. In this post, we'll share a framework and online calculator you can use to evaluate the ROI of spending more money on labeling.
Today we’re thrilled to introduce Masterful AI - a smarter, more automated way to build machine learning models. We believe that we’re at a unique point in history when we’re beginning to entrust machines with decisions that impact billions of people’s lives and livelihoods - from analyzing medical images, to driving cars, to running a manufacturing line. Yet the process to build the models powering these advances is surprisingly primitive. Machine learning may look like the space age on the outside, but it’s really the stone age on the inside. Behind the scenes, there are armies of people manually labeling training data. These armies then hand the data over to developers who manually run countless experiments to build a production-ready model. Our mission at Masterful is to bring the power and efficiency of modern software development to ML. The AutoML platform we're announcing today supports this mission by reducing labeling and shortening the time to achieve a great model. We do this in two ways: 1) we use unlabeled, augmented, and synthetic data to improve your model and 2) we automatically test and tune your training loop.