MachineLearningNotebooks/how-to-use-azureml/training
amlrelsa-ms 8373b93887 update samples from Release-209 as a part of 1.56.0 SDK stable release 2024-04-29 18:42:13 +00:00
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train-in-spark update samples from Release-153 as a part of 1.46.0 SDK stable release 2022-09-30 17:30:23 +00:00
train-on-amlcompute update samples from Release-209 as a part of 1.56.0 SDK stable release 2024-04-29 18:42:13 +00:00
train-on-local update samples from Release-209 as a part of 1.56.0 SDK stable release 2024-04-29 18:42:13 +00:00
train-on-remote-vm update samples from Release-153 as a part of 1.46.0 SDK stable release 2022-09-30 17:30:23 +00:00
using-environments update samples from Release-209 as a part of 1.56.0 SDK stable release 2024-04-29 18:42:13 +00:00
README.md update samples from Release-85 as a part of SDK release 2021-02-10 15:27:28 +00:00

README.md

Using basic training APIs

Follow these sample notebooks to learn:

  1. Train within notebook: train a simple scikit-learn model using the Jupyter kernel and deploy the model to Azure Container Service.
  2. Train on local: train a model using local computer as compute target.
  3. Train on remote VM: train a model using a remote Azure VM as compute target.
  4. Train on ML Compute: train a model using an ML Compute cluster as compute target.
  5. Train in an HDI Spark cluster: train a Spark ML model using an HDInsight Spark cluster as compute target.

Impressions