ML Studio is the GUI-based integrated development environment for constructing and operationalizing Machine Learning workflows
ML Studio (classic) offers a visual experience for machine learning.
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Important—The price in R$ is merely a reference; this is an international transaction and the final price is subject to exchange rates and the inclusion of IOF taxes. An eNF will not be issued.
US government entities are eligible to purchase Azure Government services from a licensing solution provider with no upfront financial commitment, or directly through a pay-as-you-go online subscription.
Important—The price in R$ is merely a reference; this is an international transaction and the final price is subject to exchange rates and the inclusion of IOF taxes. An eNF will not be issued.
Studio pricing
Machine Learning Studio (classic) is offered in two tiers—Free and Standard.
Features by tier are compared in the table below:
| Free | Standard | |
|---|---|---|
| Price | Free |
$- per ML studio workspace per month
$- per studio experimentation hour |
| Azure subscription | Not required | Required |
| Max number of modules per experiment | 100 | Unlimited |
| Max experiment duration | 1 hour per experiment | Up to 7 days per experiment with a maximum of 24 hours per module |
| Max storage space | 10 GB | Unlimited - BYO |
| Read data from On-Premises SQL Preview | No | Yes |
| Execution/performance | Single node | Multiple nodes |
| Production Web API | No | Yes |
| SLA | No | Yes |
Production Web API pricing
Machine Learning Studio (classic) enables you to deploy predictive analytics solutions as web services.
The deployed web services are subject to the following plans:
| Dev/Test* | Standard S1 | Standard S2 | Standard S3 | |
|---|---|---|---|---|
| Tier Price per month | $- | $- | $- | $- |
| Features | ||||
| Included transactions (per month) | 1,000 | 100,000 | 2,000,000 | 50,000,000 |
| Included compute hours (per month) | 2 | 25 | 500 | 12,500 |
| Total number of web services1 | 2 | 10 | 100 | 500 |
| Overage rates | N/A |
$- per 1,000 transactions $- per API compute hour |
$- per 1,000 transactions $- per API compute hour |
$- per 1,000 transactions $- per API compute hour |
The classic version of web services is still available at the following pricing:
- $-/production API compute hour (hourly unit)
- $-/1,000 production API transactions (transactions unit)
Hourly charges only apply to active use of the service. Where multiple metres are present they are applied concurrently.
For more information between the classic web services and the new web services, please refer to the documentation.
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Machine Learning Studio (classic)
Learn more about Machine Learning Studio (classic) features and capabilities.
Pricing calculator
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SLA
Review the Service Level Agreement for Machine Learning Studio (classic).
Documentation
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Frequently asked questions
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To copy your experiments from the Free tier to the Standard tier, follow the steps described below:
- Sign in to Azure Machine Learning Studio (classic), and make sure you can see both the Free workspace and the Standard workspace in the workspace selector in the top navigation bar.
- Switch to Free workspace if you are in the Standard workspace.
- In the experiment list view, select an experiment you’d like to copy, and click the Copy command button.
- Select the Standard workspace from the pop-up dialogue box and click the Copy button.
- Please note that all the associated datasets, trained model, etc. will be copied together with the experiment into the Standard workspace.
- You will need to re-run the experiment and republish your web service in the Standard workspace.
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Machine Learning supports high availability by pairing across two regions within the same geography. US South Central is paired with US North Central.
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Production API call times can vary significantly, generally ranging from hundreds of milliseconds to a few seconds, but may require minutes depending on the complexity of the data processing and machine learning model. The best way to estimate production API call times is to benchmark a model on Machine Learning.
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Machine Learning is a multitenant service, and actual compute resources used on the backend will vary and are optimised for performance and predictability.
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Workspace charges will be broken out separately for each applicable metre on a single note.
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