The Masterful AutoML Platform

Masterful is an AutoML platform for data and training of deep learning models. It improves your model’s accuracy - without the need for more labels - through robust implementations of semi-supervised learning, synthetic data, drop-in architectural optimizations, and metalearning techniques. These techniques are accessible individually through the Advanced API, but to further reduce the effort required to manually tune and experiment with techniques, Masterful provides the autofit algorithm. Autofit metalearns the optimal combination of techniques so that ML developers don’t need to run a manual grid search on hyperparameters.

The platform supports most types of classification, detection, and segmentation, and includes data translation layers for many standard ground truth data formats. It also supports anchor box formats from several open source detection models, including Google Object Detection API.

The entire platform is optimized for speed, with nearly every operation pushed to the GPU. The platform is currently available for Tensorflow, with PyTorch support coming soon.

Masterful is built on top of Tensorflow and Keras (PyTorch support coming soon).

Abstraction Layer



API that metalearns training and regularization policies (and some drop-in architectural choices). Built on Keras.

Keras / Pytorch Lightning

API that simplifies model architecture via deep neural network primitives like convolutions, rather than Tensorflow’s scientific computing primitives.

Tensorflow / PyTorch

API that simplifies the creation and compilation of vectorized scentific computing.


API that allows low level access to GPUs for scientific computing rather than computer graphics.


Underlying hardware to perform vectorized matrix math, useful for both computer graphics and neural networks.

Masterful provides a new API, built on top of Keras (PyTorch coming soon) to focus on an ML developer’s twin goals of training: maximum speed and maximum accuracy. This solves a common source of confusion working with deep learning frameworks: they are primarily designed to make it easy to build complex architectures. This was appropriate when advancements in architectures drove most of the state of the art improvements, but today data and training are far more relevant. For example, consider regularization. Using Keras directly, regularization might occur at the object via map calls to image transforms; within the tf.keras.Model via dropout layers or kernel regularizers; or at the optimizer level via tfa.SGDW’s decoupled weight decay. By contrast, in the Masterful API, regularization is automatically metalearned as part of autofit.

The data prep and fundamental architecture design phases are inputs to Masterful. Masterful delivers a trained model, which is the input to the deployment and monitoring phases.


The rest of the documentation is organized into these sections:


Code examples to get started.


Code examples for advanced use cases.


The thinking behind Masterful.

API Reference:

Documenting the classes, methods, and functions.


Frequently Asked Questions

Release Notes:

Changes in each release.

Indices and tables