The Azure Machine Learning service comes with several advanced capabilities, support for major frameworks, familiar data science tools, and more.
Azure Machine Learning has been built on a number of design principles to simplify and accelerate machine learning development. These design principles include enabling data scientists to use a familiar and rich set of data science tools, and simplify the use of popular machine learning and deep learning frameworks.
Other than these, Microsoft has also focused on accelerating time to value by providing end-to-end machine learning lifecycle capabilities.
Since the Azure Machine Learning service supports popular open-source frameworks like TensorFlow, PyTorch, and scikit-learn, the developers and data scientists will be able to use the tools of their choice.
To enhance the productivity, there are DevOps capabilities for machine learning that enables experiment tracking and management of models deployed in the cloud or on the edge.
Python software development kit (SDK) of the new service can be accessed from any Python environment, editors and IDEs, and Notebooks. What this means is that users will be able to access all the capabilities of Azure Machine Learning from any Python environment, IDEs like Visual Studio Code or PyCharm, or Notebooks like Jupyter and Azure Databricks.
“We built Azure Machine Learning service working closely with our customers, thousands of whom are using it every day to improve customer service, build better products and optimize their operations,” wrote Venky Veeraraghavan, Group Program Manager, Microsoft Azure, in a blog post.
Data scientists can monitor the progress of training jobs visually in near real-time, as the new service supports both local and hosted notebooks.
When customers use multiple frameworks to build models, they often face various challenges in deploying them to hardware and OS platforms. The challenges arise because the frameworks aren’t designed to be used interchangeably.
On that front, Microsoft has collaborated with industry leaders including Facebook and Amazon Web Services (AWS), to develop Open Neural Network Exchange (ONNX) specification. It will define machine learning model in an open standard format. With ONXX support, Azure Machine Learning allows users to deploy, manage, and monitor ONNX models easily.
Also read: Top 4 AI engines to look out for in 2019
Microsoft is continuously making efforts to make things easier for AI developers. In October, the company open sourced the Infer.NET machine learning engine on GitHub. It is the machine learning engine that Microsoft uses to power its own platforms including Office, Azure, and Xbox.