MachineLearningNotebooks/how-to-use-azureml/ml-frameworks
Jeff Shepherd 8fc0fa040d Remove deprecated sample notebooks 2024-11-01 11:49:20 -07:00
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fastai/fastai-with-custom-docker update samples from Release-209 as a part of 1.56.0 SDK stable release 2024-04-29 18:42:13 +00:00
keras/train-hyperparameter-tune-deploy-with-keras update samples from Release-209 as a part of 1.56.0 SDK stable release 2024-04-29 18:42:13 +00:00
pytorch update samples from Release-243 as a part of 1.58.0 SDK stable release 2024-10-16 17:50:12 +00:00
scikit-learn/train-hyperparameter-tune-deploy-with-sklearn update samples from Release-240 as a part of 1.57.0 SDK stable release 2024-08-05 21:57:46 +00:00
tensorflow Remove deprecated sample notebooks 2024-11-01 11:49:20 -07:00
using-mlflow
README.md

README.md

Training and deployment examples with ML frameworks

These sample notebooks show you how to train and deploy models with popular machine learning frameworks using Azure Machine Learning.

  1. Scikit-learn: Train, hyperparameter tune and deploy scikit-learn models.
  2. PyTorch: Train, hyperparameter tune and deploy PyTorch models. Distributed training with PyTorch.
  3. TensorFlow: Train, hyperparameter tune and deploy TensorFlow models. Distributed training with TensorFlow.
  4. Keras: Train, hyperparameter tune and deploy Keras models.
  5. Chainer: Train, hyperparameter tune and deploy Chainer models. Distributed training with Chainer.
  6. Fastai: Train, hyperparameter tune and deploy Fastai models.

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