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In this article, you learn how to set up event-driven applications, processes, or CI/CD workflows based on Azure Machine Learning events, such as failure notification emails or ML pipeline runs, when certain conditions are detected by Azure Event Grid.

Azure Machine Learning manages the entire lifecycle of machine learning process, including model training, model deployment, and monitoring. You can use Event Grid to react to Azure Machine Learning events, such as the completion of training runs, the registration and deployment of models, and the detection of data drift, by using modern serverless architectures. You can then subscribe and consume events such as run status changed, run completion, model registration, model deployment, and data drift detection within a workspace.

When to use Event Grid for event driven actions:

• Send emails on run failure and run completion
• Use an Azure function after a model is registered
• Streaming events from Azure Machine Learning to various of endpoints
• Trigger an ML pipeline when drift is detected

Note

Currently, runStatusChanged events only trigger when the run status is failed

Prerequisites

To use Event Grid, you need contributor or owner access to the Azure Machine Learning workspace you will create events for.

The event model & types

Azure Event Grid reads events from sources, such as Azure Machine Learning and other Azure services. These events are then sent to event handlers such as Azure Event Hubs, Azure Functions, Logic Apps, and others. The following diagram shows how Event Grid connects sources and handlers, but is not a comprehensive list of supported integrations.

For more information on event sources and event handlers, see What is Event Grid?.

Event types for Azure Machine Learning

Azure Machine Learning provides events in the various points of machine learning lifecycle:

Filter & subscribe to events

These events are published through Azure Event Grid. Using Azure portal, PowerShell or Azure CLI, customers can easily subscribe to events by specifying one or more event types, and filtering conditions.

When setting up your events, you can apply filters to only trigger on specific event data. In the example below, for run status changed events, you can filter by run types. The event only triggers when the criteria is met. Refer to the Azure Machine Learning event grid schema to learn about event data you can filter by.

Subscriptions for Azure Machine Learning events are protected by role-based access control (RBAC). Only contributor or owner of a workspace can create, update, and delete event subscriptions. Filters can be applied to event subscriptions either during the creation of the event subscription or at a later time.

1. Go to the Azure portal, select a new subscription or an existing one.

2. Select the filters tab and scroll down to Advanced filters. For the Key and Value, provide the property types you want to filter by. Here you can see the event will only trigger when the run type is a pipeline run or pipeline step run.

• Filter by event type: An event subscription can specify one or more Azure Machine Learning event types.

• Filter by event subject: Azure Event Grid supports subject filters based on begins with and ends with matches, so that events with a matching subject are delivered to the subscriber. Different machine learning events have different subject format.

  Event type	Subject format	Sample subject
  Microsoft.MachineLearningServices.RunCompleted	experiments/{ExperimentId}/runs/{RunId}	experiments/b1d7966c-f73a-4c68-b846-992ace89551f/runs/my_exp1_1554835758_38dbaa94
  Microsoft.MachineLearningServices.ModelRegistered	models/{modelName}:{modelVersion}	models/sklearn_regression_model:3
  Microsoft.MachineLearningServices.ModelDeployed	endpoints/{serviceId}	endpoints/my_sklearn_aks
  Microsoft.MachineLearningServices.DatasetDriftDetected	datadrift/{data.DataDriftId}/run/{data.RunId}	datadrift/4e694bf5-712e-4e40-b06a-d2a2755212d4/run/my_driftrun1_1550564444_fbbcdc0f
  Microsoft.MachineLearningServices.RunStatusChanged	experiments/{ExperimentId}/runs/{RunId}	experiments/b1d7966c-f73a-4c68-b846-992ace89551f/runs/my_exp1_1554835758_38dbaa94

• Advanced filtering: Azure Event Grid also supports advanced filtering based on published event schema. Azure Machine Learning event schema details can be found in Azure Event Grid event schema for Azure Machine Learning. Some sample advanced filterings you can perform include:

  For Microsoft.MachineLearningServices.ModelRegistered event, to filter model's tag value:

  To learn more about how to apply filters, see Filter events for Event Grid.

Consume Machine Learning events

Applications that handle Machine Learning events should follow a few recommended practices:

• As multiple subscriptions can be configured to route events to the same event handler, it is important not to assume events are from a particular source, but to check the topic of the message to ensure that it comes from the machine learning workspace you are expecting.
• Similarly, check that the eventType is one you are prepared to process, and do not assume that all events you receive will be the types you expect.
• As messages can arrive out of order and after some delay, use the etag fields to understand if your information about objects is still up-to-date. Also, use the sequencer fields to understand the order of events on any particular object.
• Ignore fields you don't understand. This practice will help keep you resilient to new features that might be added in the future.
• Failed or cancelled Azure Machine Learning operations will not trigger an event. For example, if a model deployment fails Microsoft.MachineLearningServices.ModelDeployed won't be triggered. Consider such failure mode when design your applications. You can always use Azure Machine Learning SDK, CLI or portal to check the status of an operation and understand the detailed failure reasons.

Azure Event Grid allows customers to build de-coupled message handlers, which can be triggered by Azure Machine Learning events. Some notable examples of message handlers are:

• Azure Functions
• Azure Logic Apps
• Azure Event Hubs
• Azure Data Factory Pipeline
• Generic webhooks, which may be hosted on the Azure platform or elsewhere

Set up in Azure portal

1. Open the Azure portal and go to your Azure Machine Learning workspace.

2. From the left bar, select Events and then select Event Subscriptions.

3. Select the event type to consume. For example, the following screenshot has selected Model registered, Model deployed, Run completed, and Dataset drift detected:

4. Select the endpoint to publish the event to. In the following screenshot, Event hub is the selected endpoint:

Once you have confirmed your selection, click Create. After configuration, these events will be pushed to your endpoint.

Set up with the CLI

You can either install the latest Azure CLI, or use the Azure Cloud Shell that is provided as part of your Azure subscription.

To install the Event Grid extension, use the following command from the CLI:

The following example demonstrates how to select an Azure subscription and creates e a new event subscription for Azure Machine Learning:

Examples

Example: Send email alerts

Use Azure Logic Apps to configure emails for all your events. Customize with conditions and specify recipients to enable collaboration and awareness across teams working together.

1. In the Azure portal, go to your Azure Machine Learning workspace and select the events tab from the left bar. From here, select Logic apps.

2. Sign into the Logic App UI and select Machine Learning service as the topic type.

3. Select which event(s) to be notified for. For example, the following screenshot RunCompleted.

4. You can use the filtering method in the section above or add filters to only trigger the logic app on a subset of event types. In the following screenshot, a prefix filter of /datadriftID/runs/ is used.

5. Next, add a step to consume this event and search for email. There are several different mail accounts you can use to receive events. You can also configure conditions on when to send an email alert.

6. Select Send an email and fill in the parameters. In the subject, you can include the Event Type and Topic to help filter events. You can also include a link to the workspace page for runs in the message body.

7. To save this action, select Save As on the left corner of the page. From the right bar that appears, confirm creation of this action.

Example: Data drift triggers retraining

Models go stale over time, and not remain useful in the context it is running in. One way to tell if it's time to retrain the model is detecting data drift.

This example shows how to use event grid with an Azure Logic App to trigger retraining. The example triggers an Azure Data Factory pipeline when data drift occurs between a model's training and serving datasets.

Before you begin, perform the following actions:

• Set up a dataset monitor to detect data drift in a workspace
• Create a published Azure Data Factory pipeline.

In this example, a simple Data Factory pipeline is used to copy files into a blob store and run a published Machine Learning pipeline. For more information on this scenario, see how to set up a Machine Learning step in Azure Data Factory

1. Start with creating the logic app. Go to the Azure portal, search for Logic Apps, and select create.

2. Fill in the requested information. To simplify the experience, use the same subscription and resource group as your Azure Data Factory Pipeline and Azure Machine Learning workspace.

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3. Once you have created the logic app, select When an Event Grid resource event occurs.

4. Login and fill in the details for the event. Set the Resource Name to the workspace name. Set the Event Type to DatasetDriftDetected.

5. Add a new step, and search for Azure Data Factory. Select Create a pipeline run.

6. Login and specify the published Azure Data Factory pipeline to run.

7. Save and create the logic app using the save button on the top left of the page. To view your app, go to your workspace in the Azure portal and click on Events.

Now the data factory pipeline is triggered when drift occurs. View details on your data drift run and machine learning pipeline on the new workspace portal.

Example: Deploy a model based on tags

An Azure Machine Learning model object contains parameters you can pivot deployments on such as model name, version, tag, and property. The model registration event can trigger an endpoint and you can use an Azure Function to deploy a model based on the value of those parameters.

For an example, see the https://github.com/Azure-Samples/MachineLearningSamples-NoCodeDeploymentTriggeredByEventGrid repository and follow the steps in the readme file.

Next steps

Learn more about Event Grid and give Azure Machine Learning events a try:

In machine learning, a hyperparameter is a parameter whose value is set before the learning process begins. By contrast, the values of other parameters are derived via training.

Hyperparameters can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process. An example of a model hyperparameter is the topology and size of a neural network. Examples of algorithm hyperparameters are learning rate and mini-batch size.^{[clarification needed]}

Different model training algorithms require different hyperparameters, some simple algorithms (such as ordinary least squares regression) require none. Given these hyperparameters, the training algorithm learns the parameters from the data. For instance, LASSO is an algorithm that adds a regularization hyperparameter to ordinary least squares regression, which has to be set before estimating the parameters through the training algorithm.

Considerations[edit]

The time required to train and test a model can depend upon the choice of its hyperparameters.^[1] A hyperparameter is usually of continuous or integer type, leading to mixed-type optimization problems.^[1] The existence of some hyperparameters is conditional upon the value of others, e.g. the size of each hidden layer in a neural network can be conditional upon the number of layers.^[1]

Difficulty learnable parameters[edit]

Usually, but not always, hyperparameters cannot be learned using well known gradient based methods (such as gradient descent, LBFGS) - which are commonly employed to learn parameters. These hyperparameters are those parameters describing a model representation that cannot be learned by common optimization methods but nonetheless affect the loss function. An example would be the tolerance hyperparameter for errors in support vector machines.

Untrainable parameters[edit]

Sometimes, hyperparameters cannot be learned from the training data because they aggressively increase the capacity of a model and can push the loss function to a bad minimum - overfitting to, and picking up noise, in the data - as opposed to correctly mapping the richness of the structure in the data. For example - if we treat the degree of a polynomial equation fitting a regression model as a trainable parameter - this would just raise the degree up until the model perfectly fit the data, giving small training error - but bad generalization performance.

Tunability[edit]

Most performance variation can be attributed to just a few hyperparameters.^[2][1][3] The tunability of an algorithm, hyperparameter, or interacting hyperparameters is a measure of how much performance can be gained by tuning it.^[4] For an LSTM, while the learning rate followed by the network size are its most crucial hyperparameters,^[5] batching and momentum have no significant effect on its performance.^[6]

Although some research has advocated the use of mini-batch sizes in the thousands, other work has found the best performance with mini-batch sizes between 2 and 32.^[7]

Robustness[edit]

An inherent stochasticity in learning directly implies that the empirical hyperparameter performance is not necessarily its true performance.^[1] Methods that are not robust to simple changes in hyperparameters, random seeds, or even different implementations of the same algorithm cannot be integrated into mission critical control systems without significant simplification and robustification.^[8]

Reinforcement learning algorithms, in particular, require measuring their performance over a large number of random seeds, and also measuring their sensitivity to choices of hyperparameters.^[8] Their evaluation with a small number of random seeds does not capture performance adequately due to high variance.^[8] Some reinforcement learning methods, e.g. DDPG (Deep Deterministic Policy Gradient), are more sensitive to hyperparameter choices than others.^[8]

Optimization[edit]

Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model which minimizes a predefined loss function on given test data.^[1] The objective function takes a tuple of hyperparameters and returns the associated loss.^[1]

Reproducibility[edit]

Apart from tuning hyperparameters, machine learning involves storing and organizing the parameters and results, and making sure they are reproducible.^[9] In the absence of a robust infrastructure for this purpose, research code often evolves quickly and compromises essential aspects like bookkeeping and reproducibility.^[10] Online collaboration platforms for machine learning go further by allowing scientists to automatically share, organize and discuss experiments, data, and algorithms.^[11]

A number of relevant services and open source software exist:

Services[edit]

Software[edit]

See also[edit]

References[edit]

1. ^ ^abcdefg'Claesen, Marc, and Bart De Moor. 'Hyperparameter Search in Machine Learning.' arXiv preprint arXiv:1502.02127 (2015)'. arXiv:1502.02127. Bibcode:2015arXiv150202127C.
2. ^Leyton-Brown, Kevin; Hoos, Holger; Hutter, Frank (January 27, 2014). 'An Efficient Approach for Assessing Hyperparameter Importance': 754–762 – via proceedings.mlr.press.Cite journal requires journal= (help)
3. ^'van Rijn, Jan N., and Frank Hutter. 'Hyperparameter Importance Across Datasets.' arXiv preprint arXiv:1710.04725 (2017)'. arXiv:1710.04725. Bibcode:2017arXiv171004725V.
4. ^'Probst, Philipp, Bernd Bischl, and Anne-Laure Boulesteix. 'Tunability: Importance of Hyperparameters of Machine Learning Algorithms.' arXiv preprint arXiv:1802.09596 (2018)'. arXiv:1802.09596. Bibcode:2018arXiv180209596P.
5. ^Greff, K.; Srivastava, R. K.; Koutník, J.; Steunebrink, B. R.; Schmidhuber, J. (October 23, 2017). 'LSTM: A Search Space Odyssey'. IEEE Transactions on Neural Networks and Learning Systems. 28 (10): 2222–2232. arXiv:1503.04069. doi:10.1109/TNNLS.2016.2582924. PMID27411231.
6. ^'Breuel, Thomas M. 'Benchmarking of LSTM networks.' arXiv preprint arXiv:1508.02774 (2015)'. arXiv:1508.02774. Bibcode:2015arXiv150802774B.
7. ^'Revisiting Small Batch Training for Deep Neural Networks (2018)'. arXiv:1804.07612. Bibcode:2018arXiv180407612M.
8. ^ ^abcd'Mania, Horia, Aurelia Guy, and Benjamin Recht. 'Simple random search provides a competitive approach to reinforcement learning.' arXiv preprint arXiv:1803.07055 (2018)'. arXiv:1803.07055. Bibcode:2018arXiv180307055M.
9. ^ ^ab'Greff, Klaus, and Jürgen Schmidhuber. 'Introducing Sacred: A Tool to Facilitate Reproducible Research.''(PDF). 2015.
10. ^ ^ab'Greff, Klaus, et al. 'The Sacred Infrastructure for Computational Research.''(PDF). 2017.
11. ^ ^abc'Vanschoren, Joaquin, et al. 'OpenML: networked science in machine learning.' arXiv preprint arXiv:1407.7722 (2014)'. arXiv:1407.7722. Bibcode:2014arXiv1407.7722V.
12. ^'Comet.ml – Machine Learning Experiment Management'.
13. ^Inc, Comet ML. 'comet-ml: Supercharging Machine Learning' – via PyPI.
14. ^ ^abVan Rijn, Jan N.; Bischl, Bernd; Torgo, Luis; Gao, Bo; Umaashankar, Venkatesh; Fischer, Simon; Winter, Patrick; Wiswedel, Bernd; Berthold, Michael R.; Vanschoren, Joaquin (2013). 'OpenML: A Collaborative Science Platform'. Van Rijn, Jan N., et al. 'OpenML: A collaborative science platform.' Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2013. Lecture Notes in Computer Science. 7908. pp. 645–649. doi:10.1007/978-3-642-40994-3_46. ISBN978-3-642-38708-1.
15. ^ ^ab'Vanschoren, Joaquin, Jan N. van Rijn, and Bernd Bischl. 'Taking machine learning research online with OpenML.' Proceedings of the 4th International Conference on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications-Volume 41. JMLR. org, 2015'(PDF).
16. ^ ^ab'van Rijn, J. N. Massively collaborative machine learning. Diss. 2016'. 2016-12-19.
17. ^ ^ab'OpenML'. GitHub.
18. ^'Weights & Biases for Experiment Tracking and Collaboration'.
19. ^'Monitor your Machine Learning models with PyEnv'.
20. ^Greff, Klaus (2020-01-03). 'sacred: Facilitates automated and reproducible experimental research' – via PyPI.