Blog posts

Semi-supervised learning (SSL) unlocks value in your unlabeled data, but it can be difficult to implement. Masterful can fully automate model training and apply SSL, but it also allows users to easily use SSL with their existing training code. We call this Simple SSL, and the benefits are less labeling and a more accurate model. In this post we'll show you how to try it.

Today we’re making a big leap forward in enabling new applications and insights for every enterprise that uses visual data. We’re releasing the next version of Masterful, the platform that automates model development for computer vision. Masterful now includes a low-code interface that enables developers to build models faster than ever before. Using a simple command-line interface (CLI), any developer can build production-grade models without needing to write code in TensorFlow or PyTorch. Our low-code interface also opens up CV development to an even wider universe of developers!

In a previous post we compared Masterful AI’s computer vision training platform to Google’s Vertex AI AutoML platform.  We observed significant improvements across the board from Masterful. But that comparison only considered the use of labeled data, whereas Masterful—unlike Vertex—can also leverage your unlabeled data to improve performance, using semi-supervised learning (SSL).

We’re excited to announce the v0.4.1 release of Masterful, the training platform for computer vision models!

Intro

Previously, we showed that throwing more training data at a deep learning model has rapidly diminishing returns. If doubling your labeling budget won’t move the needle, what next? Consider semi-supervised learning (SSL) to unlock the information in unlabeled data. 

We're thrilled to announce that the Masterful AutoML platform is now available on the Python Package Index for everyone to use. Installation is as easy as pip install masterful. You get the full power of our deep learning platform to train your models to peak performance without manual tuning. With Masterful, developers can focus on solving problems with ML instead of wasting time and brain cells running endless training experiments.

Intro

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.