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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. Impressions

Getting Started

Title Task Dataset Training Compute Deployment Target ML Framework Tags
Using Azure ML environments Creating and registering environments None Local None None None

Tutorials

Title Task Dataset Training Compute Deployment Target ML Framework Tags
Forecasting BikeShare Demand Forecasting BikeShare Remote None Azure ML AutoML Forecasting
Forecasting orange juice sales with deployment Forecasting Orange Juice Sales Remote Azure Container Instance Azure ML AutoML None
Forecasting orange juice sales with deployment Forecasting Orange Juice Sales Remote Azure Container Instance Azure ML AutoML None
Data drift quickdemo Filtering NOAA Remote None Azure ML Dataset, Timeseries, Drift
Datasets with ML Pipeline Train Fashion MNIST Remote None Azure ML Dataset, Pipeline, Estimator, ScriptRun
Filtering data using Tabular Timeseiries Dataset related API Filtering NOAA Local None Azure ML Dataset, Tabular Timeseries
Train with Datasets (Tabular and File) Train Iris, Diabetes Remote None Azure ML Dataset, Estimator, ScriptRun
Forecasting away from training data Forecasting None Remote None Azure ML AutoML Forecasting, Confidence Intervals
Automated ML run with basic edition features. Classification Bankmarketing AML ACI None featurization, explainability, remote_run, AutomatedML
Classification of credit card fraudulent transactions using Automated ML Classification Creditcard AML Compute None None remote_run, AutomatedML
Automated ML run with featurization and model explainability. Regression MachineData AML ACI None featurization, explainability, remote_run, AutomatedML
auto-ml-forecasting-backtest-single-model None Remote None Azure ML AutoML
Azure Machine Learning Pipeline with DataTranferStep Demonstrates the use of DataTranferStep Custom ADF None Azure ML None
Getting Started with Azure Machine Learning Pipelines Getting Started notebook for ANML Pipelines Custom AML Compute None Azure ML None
Azure Machine Learning Pipeline with AzureBatchStep Demonstrates the use of AzureBatchStep Custom Azure Batch None Azure ML None
How to use ModuleStep with AML Pipelines Demonstrates the use of ModuleStep Custom AML Compute None Azure ML None
How to use Pipeline Drafts to create a Published Pipeline Demonstrates the use of Pipeline Drafts Custom AML Compute None Azure ML None
Azure Machine Learning Pipeline with HyperDriveStep Demonstrates the use of HyperDriveStep Custom AML Compute None Azure ML None
How to Publish a Pipeline and Invoke the REST endpoint Demonstrates the use of Published Pipelines Custom AML Compute None Azure ML None
How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint Demonstrates the use of Schedules for Published Pipelines and Pipeline endpoints Custom AML Compute None Azure ML None
How to setup a versioned Pipeline Endpoint Demonstrates the use of PipelineEndpoint to run a specific version of the Published Pipeline Custom AML Compute None Azure ML None
How to use DataPath as a PipelineParameter Demonstrates the use of DataPath as a PipelineParameter Custom AML Compute None Azure ML None
How to use Dataset as a PipelineParameter Demonstrates the use of Dataset as a PipelineParameter Custom AML Compute None Azure ML None
How to use AdlaStep with AML Pipelines Demonstrates the use of AdlaStep Custom Azure Data Lake Analytics None Azure ML None
How to use DatabricksStep with AML Pipelines Demonstrates the use of DatabricksStep Custom Azure Databricks None Azure ML, Azure Databricks None
How to use KustoStep with AML Pipelines Demonstrates the use of KustoStep Custom Kusto None Azure ML, Kusto None
How to use AutoMLStep with AML Pipelines Demonstrates the use of AutoMLStep Custom AML Compute None Automated Machine Learning None
Azure Machine Learning Pipeline with CommandStep for R Demonstrates the use of CommandStep for running R scripts Custom AML Compute None Azure ML None
Azure Machine Learning Pipeline with CommandStep Demonstrates the use of CommandStep Custom AML Compute None Azure ML None
Azure Machine Learning Pipelines with Data Dependency Demonstrates how to construct a Pipeline with data dependency between steps Custom AML Compute None Azure ML None
How to use run a notebook as a step in AML Pipelines Demonstrates the use of NotebookRunnerStep Custom AML Compute None Azure ML None
Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier, leveraging MLflow auto logging MNIST Local, AML Compute Azure Container Instance Keras mlflow, keras
Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier Use MLflow with Azure Machine Learning to train and deploy PyTorch image classifier model MNIST Local, AML Compute Azure Container Instance PyTorch mlflow, pytorch
Use MLflow projects with Azure Machine Learning to train a model with local compute Use MLflow projects with Azure Machine Learning to train a model using local compute Local ScikitLearn mlflow, scikit
Use MLflow projects with Azure Machine Learning to train a model Use MLflow projects with Azure Machine Learning to train a model using azureml compute AML Compute Scikit mlflow, scikit
How to use ScriptRun with data input and output Demonstrates the use of Scriptrun with datasets Custom AML Compute None Azure ML Dataset, ScriptRun

Training

Title Task Dataset Training Compute Deployment Target ML Framework Tags
Train a model with a custom Docker image Train with custom Docker image Oxford IIIT Pet AML Compute None Pytorch None
Train a DNN using hyperparameter tuning and deploying with Keras Create a multi-class classifier MNIST AML Compute Azure Container Instance TensorFlow None
Distributed training with PyTorch Train a model using distributed training via PyTorch DistributedDataParallel CIFAR-10 AML Compute None PyTorch None
Training with hyperparameter tuning using PyTorch Train an image classification model using transfer learning with the PyTorch estimator ImageNet AML Compute Azure Container Instance PyTorch None
Training and hyperparameter tuning with Scikit-learn Train a support vector machine (SVM) to perform classification Iris AML Compute None Scikit-learn None
Hyperparameter tuning and warm start using the TensorFlow estimator Train a deep neural network MNIST AML Compute Azure Container Instance TensorFlow None
Training and hyperparameter tuning using the TensorFlow estimator Train a deep neural network MNIST AML Compute Azure Container Instance TensorFlow None
Resuming a model Resume a model in TensorFlow from a previously submitted run MNIST AML Compute None TensorFlow None
Using Tensorboard Export the run history as Tensorboard logs None None None TensorFlow None
Training in Spark Submiting a run on a spark cluster None HDI cluster None PySpark None
Train on Azure Machine Learning Compute Submit a run on Azure Machine Learning Compute. Diabetes AML Compute None None None
Train on local compute Train a model locally Diabetes Local None None None
Train in a remote Linux virtual machine Configure and execute a run Diabetes Data Science Virtual Machine None None None
Managing your training runs Monitor and complete runs None Local None None None
Tensorboard integration with run history Run a TensorFlow job and view its Tensorboard output live None Local, DSVM, AML Compute None TensorFlow None
Use MLflow with AML for a local training run Use MLflow tracking APIs together with Azure Machine Learning for storing your metrics and artifacts Diabetes Local None None None
Use MLflow with AML for a remote training run Use MLflow tracking APIs together with AML for storing your metrics and artifacts Diabetes AML Compute None None None

Deployment

Title Task Dataset Training Compute Deployment Target ML Framework Tags

Other Notebooks

Title Task Dataset Training Compute Deployment Target ML Framework Tags
configuration
fairlearn-azureml-mitigation
upload-fairness-dashboard
azure-ml-with-nvidia-rapids
auto-ml-continuous-retraining
codegen-for-autofeaturization
custom-model-training-from-autofeaturization-run
auto-ml-regression-model-proxy
auto-ml-forecasting-backtest-many-models
auto-ml-forecasting-energy-demand
auto-ml-forecasting-github-dau
auto-ml-forecasting-hierarchical-timeseries
auto-ml-forecasting-many-models
auto-ml-forecasting-univariate-recipe-experiment-settings
auto-ml-forecasting-univariate-recipe-run-experiment
auto-ml-regression
automl-databricks-local-01
automl-databricks-local-with-deployment
spark_job_on_synapse_spark_pool
spark_session_on_synapse_spark_pool
Synapse_Job_Scala_Support
Synapse_Session_Scala_Support
explain-model-on-amlcompute
save-retrieve-explanations-run-history
train-explain-model-locally-and-deploy
train-explain-model-on-amlcompute-and-deploy
training_notebook
nyc-taxi-data-regression-model-building
authentication-in-azureml
pong_rllib
rai-loan-decision
Logging APIs Logging APIs and analyzing results None None None None None
configuration
quickstart-azureml-automl
quickstart-azureml-in-10mins
quickstart-azureml-python-sdk
tutorial-1st-experiment-sdk-train
img-classification-part1-training
img-classification-part2-deploy
img-classification-part3-deploy-encrypted
tutorial-pipeline-batch-scoring-classification
regression-automated-ml