Microsoft has unveiled a new fully-managed cloud-based machine learning platform for building predictive analytics solutions, which it will preview next month.
The company said that Azure Machine Learning (ML) will bring together the capabilities of new analytics tools, algorithms developed for Microsoft products like Xbox and Bing, and years of machine learning experience into one cloud service.
It also comes with visual workflows, startup templates and the ability to publish APIs and web services in minutes to make common machine learning tasks easier.
The benefits for its partners and its customers, Microsoft says, are that they can get up and running quickly and without the costs of authoring, developing and scaling machine learning solutions.
It will allow its customers and partners can build data-driven applications to predict, forecast and change future outcomes – a process that previously took weeks and months.
Joseph Sirosh, corporate vice president of Machine Learning at Microsoft, explained: “Machine learning today is usually self-managed and on premises, requiring the training and expertise of data scientists. However, data scientists are in short supply, commercial software licenses can be expensive and popular programming languages for statistical computing have a steep learning curve.
“Even if a business could overcome these hurdles, deploying new machine learning models in production systems often requires months of engineering investment. Scaling, managing and monitoring these production systems requires the capabilities of a very sophisticated engineering organisation, which few enterprises have today.”
Microsoft’s partners have already started working with the solution, alongside their customers.
One early adopter – Corey Coscioni of business and technology consulting firm West Monroe Partners – said: “Azure ML is the future of Analytics. It seamlessly brings together statistics/mathematics with ML, AI, and advances in data storage and computing.”
"Azure ML offers a data science experience that is directly accessible to business analysts and domain experts, reducing complexity and broadening participation through better tooling," added Hans Kristiansen, Microsoft business intelligence architect at consulting firm Capgemini.
MAX451 is helping a large retail customer determine what products a customer is most likely to purchase next, based on e-commerce and bricks-and-mortar store data.
OSISoft is working with Carnegie Mellon University on real-time fault detection and the diagnosis of energy output variations across campus buildings.
It says that machine learning is helping to mitigate issues in real time and to predictively optimise energy usage and cost.
“The ease of implementation makes machine learning accessible to a larger number of investigators with various backgrounds – even non-data scientists,” commented Bertrand Lasternas, researcher at Carnegie Mellon.
This new offering continues on from CEO Satya Nadella’s focus on creating a data culture.
In April, the company released a limited public preview of the Azure Intelligent Systems Service.
The cloud-based service allows customers to connect, manage, capture and transform machine-generated data from sensors and devices, regardless of the operating system or platform they are using.
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