Microsoft Azure Machine Learning service now generally available

Solution enables users to build, train and deploy models from the cloud to the edge

Richard Humphreys | 10 December 2018

Microsoft’s Azure Machine Learning service is now generally available. The solution enables users to build, train and deploy machine learning models from the cloud to the edge.

“Automated machine learning enables data scientists of all skill levels to identify suitable algorithms and hyperparameters faster,” explains Venky Veeraraghavan, group program manager for Microsoft Azure, in a blog post announcing the service. “Support for popular open-source frameworks such as PyTorch, TensorFlow, and scikit-learn allow data scientists to use the tools of their choice. DevOps capabilities for machine learning further improve productivity by enabling experiment tracking and management of models deployed in the cloud and on the edge. All these capabilities can be accessed from any Python environment running anywhere, including data scientists’ workstations.”

A number of high-profile customers are already leveraging the service. TAL, a life insurance company in Australia, for example, is embracing artificial intelligence (AI) to improve quality assurance and customer experience. Traditionally, TAL’s quality assurance team could only review a randomly selected 2-3% of cases. Using Azure Machine Learning service, it is now able to review 100% of cases.

“Azure Machine Learning regularly lets TAL’s data scientists deploy models within hours rather than weeks or months – delivering faster outcomes and the opportunity to roll out many more models than was previously possible. There is nothing on the market that matches Azure Machine Learning in this regard,” said Gregor Pacnik, TAL’s innovation delivery manager.

Meanwhile Elastacloud, a London-based data science consultancy, is using the Azure Machine Learning service to build and run the Elastacloud Energy BSUoS Forecast service, an AI-powered solution that helps alternative energy providers to better predict demand and reduce costs.

“With Azure Machine Learning, we support BSUoS Forecast with no virtual machines and nothing to manage. We built a highly automated service that hides its complexity inside serverless boxes,” said Andy Cross, Elastacloud’s chief operating officer.