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Build models, not experiments

Building machine learning models shouldn't mean running endless training experiments.  Masterful brings the power and efficiency of modern software development to ML, so developers can get their models into production faster.  On average Masterful can reduce model error by at least 40%, and shorten time to deployment up to 50%.

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Simple API

The Masterful AutoML platform has a simple API to analyze your data and train your model.  

  • Analyze.  Pass in your existing labeled and unlabeled training, and Masterful runs a one-time analysis of your dataset.  It applies a comprehensive test of semi-supervised learning techniques, augmentations, synthetic data, and advanced training algorithms.  This produces a training policy for improving your model accuracy. 
  • Train.  Apply the training policy in your existing environment.  Masterful supports computer vision models for classification, object detection, and semantic segmentation.

An integrated, extensible platform

  • Semi-supervised learning.  Get value from your unlabeled data through Masterful's semi-supervised learning algorithms, including contrastive learning techniques and noisy student training to improve in-domain accuracy and adapting models to new domains.
  • Augmentations.  Classic spatial and color-space augmentations and advanced soft-label augmentation techniques, rapidly evaluated in combinations using Frechet Inception Distance based clustering.
  • Synthetic data.  Generated by GANs and Neural Style Transfer, and automatic tuning of the optimal ratio of synthetic-to-real data.
  • Advanced training algorithms.  Ensembling, Knowledge Distillation, and more.  Plug in your own algorithms for Masterful to test and visualize alongside other techniques.

Transparency and control

Unlike other AutoML platforms, Masterful is transparent, and you retain full control over your data, model, and development environment. Train with your model, your training data, run on-prem or in your private cloud, and measure with your preferred metrics. 

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Optimal training policies

Masterful shows the search paths it took in analyzing your training data, and the recommended policy to maximize model performance.


Forecasted accuracy improvements

Masterful creates a forecast of model accuracy improvements that come from applying the training policy.

Once generated, the policy can be applied automatically during the training loop with minimal resource overhead since all Masterful operations are layerized and can run in multi-GPU environments.

Training insights

Masterful also shows the impact of the training policy broken out per class, revealing insights about quality of the original training data and it's interaction with the model.

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Masterful benchmark reports

See how Masterful improves model performance on example datasets.

Benchmark Report

45% Error Reduction

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Street View House Numbers (SVHN)
Benchmark Report

58% Error Reduction 

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Manufacturing Benchmark Report

57% Error Reduction

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Get a free analysis of your model