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.
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.
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.