Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Fixing url links #1851

Open
wants to merge 3 commits into
base: master
Choose a base branch
from
Open

Fixing url links #1851

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
a8ab2f2
Update Readme #1850
cosmiccamel Nov 7, 2022
0be65cd
Update README.md
cosmiccamel Nov 7, 2022
c363bdc
Update links
cosmiccamel Nov 7, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 13 additions & 19 deletions how-to-use-azureml/automated-machine-learning/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -109,36 +109,36 @@ jupyter notebook
## Classification
- **Classify Credit Card Fraud**
- Dataset: [Kaggle's credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
- **[Jupyter Notebook (remote run)](classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)**
- **[Jupyter Notebook (remote run)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)**
- run the experiment remotely on AML Compute cluster
- test the performance of the best model in the local environment
- **[Jupyter Notebook (local run)](local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)**
- **[Jupyter Notebook (local run)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)**
- run experiment in the local environment
- use Mimic Explainer for computing feature importance
- deploy the best model along with the explainer to an Azure Kubernetes (AKS) cluster, which will compute the raw and engineered feature importances at inference time
- **Predict Term Deposit Subscriptions in a Bank**
- Dataset: [UCI's bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
- **[Jupyter Notebook](classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)**
- **[Jupyter Notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)**
- run experiment remotely on AML Compute cluster to generate ONNX compatible models
- view the featurization steps that were applied during training
- view feature importance for the best model
- download the best model in ONNX format and use it for inferencing using ONNXRuntime
- deploy the best model in PKL format to Azure Container Instance (ACI)
- **Predict Newsgroup based on Text from News Article**
- Dataset: [20 newsgroups text dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html)
- **[Jupyter Notebook](classification-text-dnn/auto-ml-classification-text-dnn.ipynb)**
- **[Jupyter Notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb)**
- AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data
- AutoML will use Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used
- Bidirectional Long-Short Term neural network (BiLSTM) will be utilized when a CPU compute is used, thereby optimizing the choice of DNN

## Regression
- **Predict Performance of Hardware Parts**
- Dataset: Hardware Performance Dataset
- **[Jupyter Notebook](regression/auto-ml-regression.ipynb)**
- **[Jupyter Notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb)**
- run the experiment remotely on AML Compute cluster
- get best trained model for a different metric than the one the experiment was optimized for
- test the performance of the best model in the local environment
- **[Jupyter Notebook (advanced)](regression/auto-ml-regression.ipynb)**
- **[Jupyter Notebook (advanced)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb)**
- run the experiment remotely on AML Compute cluster
- customize featurization: override column purpose within the dataset, configure transformer parameters
- get best trained model for a different metric than the one the experiment was optimized for
Expand All @@ -148,41 +148,35 @@ jupyter notebook
## Time Series Forecasting
- **Forecast Energy Demand**
- Dataset: [NYC energy demand data](http://mis.nyiso.com/public/P-58Blist.htm)
- **[Jupyter Notebook](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)**
- **[Jupyter Notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)**
- run experiment remotely on AML Compute cluster
- use lags and rolling window features
- view the featurization steps that were applied during training
- get the best model, use it to forecast on test data and compare the accuracy of predictions against real data
- **Forecast Orange Juice Sales (Multi-Series)**
- Dataset: [Dominick's grocery sales of orange juice](forecasting-orange-juice-sales/dominicks_OJ.csv)
- **[Jupyter Notebook](forecasting-orange-juice-sales/dominicks_OJ.csv)**
- Dataset: [Dominick's grocery sales of orange juice](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv)
- **[Jupyter Notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)**
- run experiment remotely on AML Compute cluster
- customize time-series featurization, change column purpose and override transformer hyper parameters
- evaluate locally the performance of the generated best model
- deploy the best model as a webservice on Azure Container Instance (ACI)
- get online predictions from the deployed model
- **Forecast Demand of a Bike-Sharing Service**
- Dataset: [Bike demand data](forecasting-bike-share/bike-no.csv)
- **[Jupyter Notebook](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)**
- Dataset: [Bike demand data](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv)
- **[Jupyter Notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)**
- run experiment remotely on AML Compute cluster
- integrate holiday features
- run rolling forecast for test set that is longer than the forecast horizon
- compute metrics on the predictions from the remote forecast
- **The Forecast Function Interface**
- Dataset: Generated for sample purposes
- **[Jupyter Notebook](forecasting-forecast-function/auto-ml-forecasting-function.ipynb)**
- **[Jupyter Notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb)**
- train a forecaster using a remote AML Compute cluster
- capabilities of forecast function (e.g. forecast farther into the horizon)
- generate confidence intervals
- **Forecast Beverage Production**
- Dataset: [Monthly beer production data](forecasting-beer-remote/Beer_no_valid_split_train.csv)
- **[Jupyter Notebook](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)**
- train using a remote AML Compute cluster
- enable the DNN learning model
- forecast on a remote compute cluster and compare different model performance
- **Continuous Retraining with NOAA Weather Data**
- Dataset: [NOAA weather data from Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/)
- **[Jupyter Notebook](continuous-retraining/auto-ml-continuous-retraining.ipynb)**
- **[Jupyter Notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb)**
- continuously retrain a model using Pipelines and AutoML
- create a Pipeline to upload a time series dataset to an Azure blob
- create a Pipeline to run an AutoML experiment and register the best resulting model in the Workspace
Expand Down