MachineLearningNotebooks/index.md

Index

Azure Machine Learning is a cloud service that you use to train, deploy, automate, and manage machine learning models. This index should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content.

Getting Started

Title	Task	Dataset	Training Compute	Deployment Target	ML Framework	Tags

Tutorials

Title	Task	Dataset	Training Compute	Deployment Target	ML Framework	Tags

| ⭐Use pipelines for batch scoring | Batch scoring | None | AmlCompute | Published pipeline | Azure ML Pipelines | None |

Training

Title	Task	Dataset	Training Compute	Deployment Target	ML Framework	Tags

Deployment

Title	Task	Dataset	Training Compute	Deployment Target	ML Framework	Tags

Other Notebooks

Title	Task	Dataset	Training Compute	Deployment Target	ML Framework	Tags

| configuration | | | | | | |

| azure-ml-with-nvidia-rapids | | | | | | |

| auto-ml-classification | | | | | | |

| auto-ml-classification-bank-marketing | | | | | | |

| auto-ml-classification-credit-card-fraud | | | | | | |

| auto-ml-classification-with-deployment | | | | | | |

| auto-ml-classification-with-onnx | | | | | | |

| auto-ml-classification-with-whitelisting | | | | | | |

| auto-ml-dataset | | | | | | |

| auto-ml-dataset-remote-execution | | | | | | |

| auto-ml-exploring-previous-runs | | | | | | |

| auto-ml-forecasting-bike-share | | | | | | |

| auto-ml-forecasting-energy-demand | | | | | | |

| auto-ml-forecasting-orange-juice-sales | | | | | | |

| auto-ml-missing-data-blacklist-early-termination | | | | | | |

| auto-ml-model-explanation | | | | | | |

| auto-ml-regression | | | | | | |

| auto-ml-regression-concrete-strength | | | | | | |

| auto-ml-regression-hardware-performance | | | | | | |

| auto-ml-remote-amlcompute | | | | | | |

| auto-ml-remote-amlcompute-with-onnx | | | | | | |

| auto-ml-sample-weight | | | | | | |

| auto-ml-sparse-data-train-test-split | | | | | | |

| auto-ml-sql-energy-demand | | | | | | |

| auto-ml-sql-setup | | | | | | |

| auto-ml-subsampling-local | | | | | | |

| build-model-run-history-03 | | | | | | |

| deploy-to-aci-04 | | | | | | |

| deploy-to-aks-existingimage-05 | | | | | | |

| ingest-data-02 | | | | | | |

| installation-and-configuration-01 | | | | | | |

| automl-databricks-local-01 | | | | | | |

| automl-databricks-local-with-deployment | | | | | | |

| aml-pipelines-use-databricks-as-compute-target | | | | | | |

| automl_hdi_local_classification | | | | | | |

| model-register-and-deploy | | | | | | |

| register-model-deploy-local-advanced | | | | | | |

| register-model-deploy-local | | | | | | |

| accelerated-models-object-detection | | | | | | |

| accelerated-models-quickstart | | | | | | |

| accelerated-models-training | | | | | | |

| model-register-and-deploy | | | | | | |

| register-model-deploy-local-advanced | | | | | | |

| register-model-deploy-local | | | | | | |

| enable-app-insights-in-production-service | | | | | | |

| enable-data-collection-for-models-in-aks | | | | | | |

| onnx-convert-aml-deploy-tinyyolo | | | | | | |

| onnx-inference-facial-expression-recognition-deploy | | | | | | |

| onnx-inference-mnist-deploy | | | | | | |

| onnx-modelzoo-aml-deploy-resnet50 | | | | | | |

| onnx-train-pytorch-aml-deploy-mnist | | | | | | |

| production-deploy-to-aks | | | | | | |

| production-deploy-to-aks-gpu | | | | | | |

| register-model-create-image-deploy-service | | | | | | |

| explain-model-on-amlcompute | | | | | | |

| save-retrieve-explanations-run-history | | | | | | |

| train-explain-model-locally-and-deploy | | | | | | |

| train-explain-model-on-amlcompute-and-deploy | | | | | | |

| advanced-feature-transformations-explain-local | | | | | | |

| explain-binary-classification-local | | | | | | |

| explain-multiclass-classification-local | | | | | | |

| explain-regression-local | | | | | | |

| simple-feature-transformations-explain-local | | | | | | |

| aml-pipelines-data-transfer | | | | | | |

| aml-pipelines-getting-started | | | | | | |

| aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable | | | | | | |

| aml-pipelines-how-to-use-estimatorstep | | | | | | |

| aml-pipelines-how-to-use-pipeline-drafts | | | | | | |

| aml-pipelines-parameter-tuning-with-hyperdrive | | | | | | |

| aml-pipelines-publish-and-run-using-rest-endpoint | | | | | | |

| aml-pipelines-setup-schedule-for-a-published-pipeline | | | | | | |

| aml-pipelines-setup-versioned-pipeline-endpoints | | | | | | |

| aml-pipelines-showcasing-datapath-and-pipelineparameter | | | | | | |

| aml-pipelines-use-adla-as-compute-target | | | | | | |

| aml-pipelines-use-databricks-as-compute-target | | | | | | |

| aml-pipelines-with-automated-machine-learning-step | | | | | | |

| aml-pipelines-with-data-dependency-steps | | | | | | |

| nyc-taxi-data-regression-model-building | | | | | | |

| pipeline-batch-scoring | | | | | | |

| pipeline-style-transfer | | | | | | |

| authentication-in-azureml | | | | | | |

| train-hyperparameter-tune-deploy-with-chainer | | | | | | |

| distributed-chainer | | | | | | |

| train-hyperparameter-tune-deploy-with-pytorch | | | | | | |

| distributed-pytorch-with-horovod | | | | | | |

| distributed-pytorch-with-nccl-gloo | | | | | | |

| train-hyperparameter-tune-deploy-with-sklearn | | | | | | |

| train-hyperparameter-tune-deploy-with-tensorflow | | | | | | |

| distributed-tensorflow-with-horovod | | | | | | |

| distributed-tensorflow-with-parameter-server | | | | | | |

| train-tensorflow-resume-training | | | | | | |

| azure-ml-datadrift | | | | | | |

| Logging APIs | Logging APIs and analyzing results | | None | None | None | None |

| manage-runs | | | | | | |

| tensorboard | | | | | | |

| deploy-model | | | | | | |

| train-and-deploy-pytorch | | | | | | |

| train-local | | | | | | |

| train-remote | | | | | | |

| logging-api | | | | | | |

| manage-runs | | | | | | |

| train-hyperparameter-tune-deploy-with-sklearn | | | | | | |

| train-in-spark | | | | | | |

| train-on-amlcompute | | | | | | |

| train-on-local | | | | | | |

| train-on-remote-vm | | | | | | |

| train-within-notebook | | | | | | |

| using-environments | | | | | | |

| distributed-chainer | | | | | | |

| distributed-cntk-with-custom-docker | | | | | | |

| distributed-pytorch-with-horovod | | | | | | |

| distributed-tensorflow-with-horovod | | | | | | |

| distributed-tensorflow-with-parameter-server | | | | | | |

| export-run-history-to-tensorboard | | | | | | |

| how-to-use-estimator | | | | | | |

| notebook_example | | | | | | |

| tensorboard | | | | | | |

| train-hyperparameter-tune-deploy-with-chainer | | | | | | |

| train-hyperparameter-tune-deploy-with-keras | | | | | | |

| train-hyperparameter-tune-deploy-with-pytorch | | | | | | |

| train-hyperparameter-tune-deploy-with-tensorflow | | | | | | |

| train-tensorflow-resume-training | | | | | | |

| new-york-taxi | | | | | | |

| new-york-taxi_scale-out | | | | | | |

| add-column-using-expression | | | | | | |

| append-columns-and-rows | | | | | | |

| assertions | | | | | | |

| auto-read-file | | | | | | |

| cache | | | | | | |

| column-manipulations | | | | | | |

| column-type-transforms | | | | | | |

| custom-python-transforms | | | | | | |

| data-ingestion | | | | | | |

| data-profile | | | | | | |

| datastore | | | | | | |

| derive-column-by-example | | | | | | |

| external-references | | | | | | |

| filtering | | | | | | |

| fuzzy-group | | | | | | |

| impute-missing-values | | | | | | |

| join | | | | | | |

| label-encoder | | | | | | |

| min-max-scaler | | | | | | |

| one-hot-encoder | | | | | | |

| open-save-dataflows | | | | | | |

| quantile-transformation | | | | | | |

| random-split | | | | | | |

| replace-datasource-replace-reference | | | | | | |

| replace-fill-error | | | | | | |

| secrets | | | | | | |

| semantic-types | | | | | | |

| split-column-by-example | | | | | | |

| subsetting-sampling | | | | | | |

| summarize | | | | | | |

| working-with-file-streams | | | | | | |

| writing-data | | | | | | |

| getting-started | | | | | | |

| datasets-diff | | | | | | |

| file-dataset-img-classification | | | | | | |

| tabular-dataset-tutorial | | | | | | |

| tabular-timeseries-dataset-filtering | | | | | | |

| train-with-datasets | | | | | | |

| configuration | | | | | | |

| img-classification-part1-training | | | | | | |

| img-classification-part2-deploy | | | | | | |

| regression-automated-ml | | | | | | |

| tutorial-1st-experiment-sdk-train | | | | | | |