Blog posts

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!

Once your training runs become material in terms of wall-clock time and hardware budget, it's time to look at improving your batch size. If your batch size is too small, your training runs are taking longer than necessary and you are wasting money. And if your batch size is too large, you are training with more expensive hardware than you need.

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. 

Percent top-1 error reductions achieved when moving from Google Vertex to Masterful AI. 

Version 0.4 of Masterful

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.