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