Organizations looking to build, train and deploy machine learning on the Microsoft Azure cloud are natural candidates for Microsoft’s AI platform. It delivers automated machine learning, DevOps for machine learning and a tool-agnostic Python SDK. The product can pull data from custom applications as well as BI tools, such as Microsoft Excel and tap Microsoft technology, such as Cortana Intelligence, to produce insights and actionable results.
The interactive workspace offers a visually oriented drag-and-drop environment where users pull in datasets and connect them to modules that generate machine learning functions. The environment also allows users to edit and refine models before publishing them as a web service. Microsoft Azure Machine Learning Studio requires no programming skills. It is designed for all user groups and Azure meets major compliance standards, such as HIPAA and PCI. Gartner rated the machine learning vendor a “Visionary” in its MQ.
Machine Learning Studio offers a robust set of tools designed to develop, deploy and manage machine learning projects. It incorporates open source Jupyter Notebooks, sample datasets and algorithms, and predesigned modules to aid in project development and management. The platform allows users to deploy applications and predictive models as a web service from Machine Learning Studio.
This includes Azure Data Factory, Azure Stream Analytics, Azure HDInsight, Azure Data Lake and Power BI. The environment supports both supervised and unsupervised learning. The environment also supports Python, R Script and open source Scikit-learn, TensorFlow, PyTorch, CNTK, and MXNet.
The platform also supports Docker containers. This produces an overall framework that is flexible and highly scalable. Machine Learning Studio provides comprehensive features across the full range of descriptive, diagnostic, predictive and prescriptive analytic types.
Business analysts, developers and data scientists.
Graphical drag-and-drop. Command line.
Python, R Script and open source Scikit-learn, TensorFlow, PyTorch, CNTK, and MXNet. Accommodates PowerShell module and PowerShell cmdlets.
Most data formats and file types. Works with Jupyter Notebooks, Visual Studio Code and PyCharm.
Accommodates numerous open source tools and components. Tightly integrated with Azure cloud services.
Offers strong visualization capabilities and logging functions for web services.
Microsoft offers a free tier with limited capabilities and usage time. A standard plan starts at $9.99 per month per seat with an additional $1 per studio experimentation hour. Production web API pricing are based on tiers that range from $0 to just under $10,000 per month, depending on the number of transactions, compute hours and web services used. Overage rates also apply.
| Vendor and features | Microsoft Azure Machine Learning |
|---|---|
| ML Focus | Automated ML platform running on hosted Azure Cloud. |
| Key Features and capabilities | Strong descriptive, diagnostic, predictive and prescriptive analytics tools and capabilities. Strong support for open source ML tools and scripting. |
| User comments | High ratings at Gartner Peer Review. Some complaints about support. |
| Pricing and licensing | Tiered pricing based on users and usage. Ranges from near zero to tens of thousands per user annually. |
| Vendor and features | Microsoft Azure Machine Learning |
|---|---|
| ML Focus | Automated ML platform running on hosted Azure Cloud. |
| Key Features and capabilities | Strong descriptive, diagnostic, predictive and prescriptive analytics tools and capabilities. Strong support for open source ML tools and scripting. |
| User comments | High ratings at Gartner Peer Review. Some complaints about support. |
| Pricing and licensing | Tiered pricing based on users and usage. Ranges from near zero to tens of thousands per user annually. |
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