Azure Machine Learning Webinar recap: A sneak peek at the process and possibilities of AML

The ‘Machine Learning lifecycle management and possibilities with Azure Machine Learning’ webinar presented exciting insights and an interesting demo.

Today’s organisations are witnessing an accelerated pace in building Machine Learning-fuelled solutions, so much so that ML has quickly become the most acquired skill in India on online learning platforms. An implicit requirement for the teams involved in an organisation is working collaboratively while constantly building and managing a large number of models.

To that end, Microsoft India hosted a webinar titled ‘Machine Learning lifecycle management and possibilities with Azure Machine Learning’, featuring Aruna Chakkirala, Senior Cloud Solutions Architect, Microsoft India, in order to show the benefits of Azure Machine Learning and how it enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models.

The webinar came with some amazing insights about Azure Machine Learning (AML) and its properties, along with a quick demo.

Here’s what the webinar covered:

Customer promises

“The idea is to make machine learning available to data scientists and developers of all skill levels, as well as provide an end-to-end lifecycle management for machine learning through MLOps. It also enables responsible and trustworthy development of machine learning solutions through responsible AI/ML capabilities which provides transparency and explainability for the models,” said Aruna.

Azure Machine Learning is comprehensive, giving users end-to-end capabilities, extensive ML platform abilities in Azure, which empowers data scientists and developers with a wide range of predictive experiences for building, training, and deploying ML models both securely and at scale.

The ML lifecycle

Aruna next explained the ML lifecycle. When it comes to creating ML platforms, the process begins with data scientists and data engineers working in tandem, figuring out what part of the data is interesting. The data scientist will then take the insights to build a model from it. That is followed by the registry of the model. And finally, users can go ahead and release this model into production. The model will need to be monitored periodically. Because once the model is monitored, you’ll get insights about how it fares in the real world scenario.

Azure Machine Learning

AML is a set of cloud services combined with an AML studio interface and also an SDK which brings together the power of what you need to build services and take them into production. “It enables users to prepare the data, build, train, manage, track, and deploy models,” Aruna said.


The AutoML option in AML studio is a quick and easy way to build models, primarily aimed at the citizen data scientist. All it requires is a dataset, and using the wizard to set up a few configurations, AutoML does all the work in building multiple models, identifying the best model based on the metrics and providing a view of the multiple runs. All this is possible in just a few clicks.

Azure ML Designer

AML Designer is an UI interface that enables users to build machine learning pipelines with drag-n-drop experience and simplify the publishing and deployment of pipelines.

It helps users connect to their own data with ease, hundreds of pre-built components help build and train models without writing code, it helps automate model validation, evaluation and interpretation in their pipeline and enables deployment of models and publishment of endpoints with a few clicks.

Azure ML Service workspace comes with a lot of components such as models, experiments, pipelines, compute target, environments, deployment, datastores and data labeling.

AML Demo

Aruna then conducted a quick demo, showing the components of AML. The ML service has a number of components within it, such as values, resource group, storage, studio web url, registry, application insights and ML flow.

Coming to AML studio, it too has various core components. It has the option to provide compute, where users are creating the training resources. The studio also comes with curated environments, data store, date labeling, and link services. This demo was followed by a quick guided demo of AutoML as well.

Aruna added that AML can implement an end-to-end ML lifecycle. “It ties down the entire ML lifecycle for us. Every stage of the ML lifecycle has been tied together within the whole spectrum of components,” she said.

Workflow steps:

1. Develop machine learning training scripts in Python, using autoML, Designer, Notebooks, etc.

2. Create and configure a compute target.

3. Submit the scripts to the configured compute target to run in that environment. During training, the compute target stores run records to a datastore. There, the records are saved to an experiment.

4. Review the experiment for logged metrics from the current and past runs.

5. Once a satisfactory run is found, register the persisted model in the registry.

6. Develop a scoring script.

7. Create an image and register it in the image registry.

8. Deploy the image as a web service in Azure.

9. Monitor the model in production and identify when further improvements are required.

Deploying your model

There are three ways to deploy your model in Azure ML - Through real-time interference such as HTTP endpoints, batch interference, and through managed endpoints.


Monitoring on AML has 23 metrics that you can choose from, across various categories like model, resource, run and quota. Users can look at monitoring from two different perspectives:

1. Monitoring as an administrator, which means monitoring the health of the resource, monitoring compute quota, etc.

2. Monitoring as a data scientist or developer, which means monitoring training runs, tracking experiments and visualising runs.


“You don't want to be repeating the same steps again and that’s where MLOps comes into play. It removes the drudgery of repeated processes,” said Aruna. It brings DevOps’ principles into the ML world. It brings together people, processes, and platforms to automate ML-infused software delivery and provide continuous value to users.

MLOps comes with multiple benefits such as automation and observability, validation and reproducibility.

Responsible ML

Aruna noted that model interpretability and fairness are the cornerstones of Azure Machine Learning’s AI/ML offerings. “As machine learning becomes ubiquitous in decision making, it becomes extremely necessary to provide tools which can bring out model transparency,” she said.


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