The Masterful AutoML Platform¶
Masterful is AutoML for data and training.
The documentation is organized into these sections:
- Tutorials
Code examples to get started.
- Guides
Code examples for advanced use cases.
- Concepts
The thinking behind Masterful.
- API Reference:
Documenting the classes, methods, and functions.
- FAQ:
Frequently Asked Questions
- Release Notes:
Changes in each release.
- API Reference: Autofit
- API Reference: Spec
- API Reference: Advanced
- masterful.core.fit
- masterful.core.ensemble
- masterful.core.distill
- masterful.core.pretrain
- masterful.core.adapt
- masterful.core.FitReport
- masterful.core.EnsembleReport
- masterful.core.find_fit_policy
- masterful.core.find_standard_loss
- masterful.core.find_batch_size
- masterful.core.find_max_learning_rate
- masterful.core.find_optimizer_policy
- masterful.core.find_augmentation_policy
- API Reference: Policies
- FAQ
- Frequently Asked Questions
- How does Masterful fit into my training workflow?
- Where do I run Masterful?
- What are the minimum system requirements to run Masterful?
- Are there versions of Masterful other than Python?
- What machine learning use cases does Masterful support?
- What machine learning frameworks does Masterful support?
- Do I use my own model, or do you pick an architecture for me?
- How can I see what Masterful is doing to my data and model?
- What data types does Masterful support?
- How long does Masterful’s analyze phase take to run?
- When do I use autofit, and when should I use advanced functions on their own?
- Does Masterful slow down my training loop?
- How much labeled data do I need to get started?
- How much unlabeled data do I need to get started?
- When should I use ensembling?
- When should I distill a model?
- Does my model need a minimum level of performance in order to see benefits from Masterful?
- How does Masterful relate to CUDA, Tensorflow, PyTorch, Keras, and AutoML?
- Frequently Asked Questions