Blog posts

Deep learning has opened the door to tackling many real-world computer vision problems. But building and deploying Deep Learning models has always been a tedious task of labeling the entire dataset, finding the right hyperparameters, determining a data augmentation policy, and then deploying the model.

Masterful makes building and deploying Deep Learning models orders of magnitude easier. All you need to get started is to label a small fraction of your image data. Masterful’s meta-learner finds the optimal hyperparameters, augmentation policy, and trains the model both on labeled and unlabeled data. 

In this post, we’ll show how easy it is to solve a real-world computer vision problem using Masterful.

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.