Azure Machine Learning studio - a web interface for managing the machine learning lifecycle

Microsoft

Jul 15, 2020

Machine learning is a complex and task heavy art, be it cleaning data, creating new models, deploying models, managing a model repository, or automating the entire CI/CD pipeline for machine learning.

As more companies embark on the journey of machine learning in everything they do, Microsoft Azure Machine Learning provides them with enterprise-grade capabilities to accelerate the machine learning lifecycle and empowers developers and data scientists of all skill levels to build, train, deploy, and manage models responsibly and at scale.

Azure Machine Learning studio is the web user interface of Azure Machine Learning, enabling data scientists to complete their end-to-end machine learning lifecycle, from cleaning and labeling data, to training and deploying models using cloud scalable compute, in a single enterprise-ready tool.

We are excited to announce that Azure Machine Learning studio is now generally available worldwide, supporting 18 languages and over 30 locales!

Azure Machine Learning studio caters to all skill levels, with authoring tools such as the automated machine learning user interface to train and deploy models in a click of a button, and the drag and drop designer to create ML pipelines using a visual interface. All resources and assets created during the ML process – notebooks, models, pipelines, are all available for team collaboration under one roof.

With this release, studio is even more comprehensive and easy to use

Notebooks: Intellisense, checkpoints, tabs, editing without compute, updated file operations, improved kernel reliability, and many more. Read more about Azure machine learning studio notebooks here.

Notebooks are integrated into Azure Machine Learning studio

Experimentation: Compare multiple runs graphically using an improved charting visualization experience including chart smoothing, displaying aggregated data and more.

Charts and metrics for tracking and analyzing runs

Security: Granular Role Based Access Controls (RBAC) are now supported (in preview) out of the box for the most common actions in your studio workspace. Specific actions or controls will now be hidden based on your role assignment automatically as setup by your IT Admins.

Compute: Compute instance has tons of improvements in quality, reliability, availability, provisioning latency, and user experience:

New enterprise readiness and administrator capabilities:

REST API and CLI support to help automate creation and management of compute instance
ARM template support for provisioning compute instance with sample template documented and downloadable from UI
Ability for admin to create compute instance on behalf of other users and assign to them through ARM template and REST API. Data scientists do not need to have create/delete RBAC permissions and can access Jupyter, JupyterLab, RStudio, use compute instance from integrated notebooks, and can start/stop/restart compute instances (this is in preview).
Validating user subnet NSG rules in virtual network for improved compute instance creation.
Encryption in transit using TLS 1.2

More information available in the updated compute creation panel

Designer (preview): Improved performance and reliability. Updates to user experience and new features:

New graph engine, with new-style modules. Modules have colored side bars to show the status and can be resized.
New asset library, to split Datasets, Modules, Models into 3 tabs
Output setting. Enable user to set module output datastores.
New modules:
Computer Vision: Support image dataset preprocessing, and train PyTorch models (ResNet/DenseNet), and score for image classification
Recommendation: Support Wide&Deep recommender

New style to Modules in the drag-and-drop Designer

Data Labeling: Create, manage, and monitor labeling projects directly inside the studio web experience. Coordinate data, labels, and team members to efficiently manage labeling tasks. Supports image classification, either multi-label or multi-class, and object identification with bounding boxes.

The machine learning assisted labeling feature (Preview) lets you trigger automatic machine learning models to accelerate the labeling task.

Learn more about Azure Machine Learning data labeling in this blog post.

Data labeling updated style and machine learning assisted labeling

Fairlearn (preview): Azure Machine Learning is used for managing the artifacts in your model training and deployment process.

With the new fairness capabilities, users can store and track their models’ fairness (disparity) insights in Azure Machine Learning studio, easily share their models’ fairness learnings among different stakeholders. Beyond logging fairness insights within Azure Machine Learning run history, users can load Fairlearn’s visualization dashboard in studio to interact with mitigated or original models’ predictions and fairness insights, select a pleasant model, and register/deploy the model for scoring time.

Fairlearn visualization now available as preview in the studio

Automated machine learning user interface (preview) Automated machine learning is the process of automating the time-consuming, iterative tasks of machine learning model development to enable non data scientists to operationalize their machine learning models.

The new Data Guardrails helps fix and alert users of potential data issues. The model details tab includes key information around the best model and the run. There is more control over which visualizations are generated - choose a metric of interest and visualizations pertaining to that metric will display.

Data guardrails in automated machine learning will alert for issues in the data and even fix some of them