Microsoft Azure is one of the three main cloud computing providers and has a wide range of capabilities including compute, storage, analytics, and networking. It is the second largest cloud computing platform by market share, according to the Synergy Research Group, which tracks quarterly worldwide revenues for all major cloud providers.
Microsoft Azure: AI and ML History
Microsoft launched Azure in 2010 as an extension of Windows OS, initially designed to be a new system of distribution. It wasn’t until 2013 that the company pivoted to cloud compute and storage, and the name was changed a year later from Windows Azure to Microsoft Azure, further separating the service from the operating system.
In 2014, Microsoft launched the Azure Machine Learning public preview, the company’s first foray into artificial intelligence through Azure. This was also the year Satya Nadella took over as CEO of Microsoft from Steve Ballmer, with one of his key missions to improve Microsoft’s cloud portfolio.
This goal has paid off for Nadella, with Microsoft generating over $60 billion revenue from its cloud services in 2021. It is also one of Microsoft’s highest income divisions, contributing a lot to the company’s overall $61.3 billion net income in 2021.
Microsoft now hosts dozens of AI and ML offerings for organizations that require special processing speed to run the algorithms and routines associated with artificial intelligence development.
See also: Microsoft Azure: A Quick View
AI and ML Offerings
The cloud provider has an expansive selection of artificial intelligence and machine learning offerings, and Microsoft is adding more tools and solutions to the platform every month, alongside third-party partner solutions that can be integrated into Microsoft Azure’s cloud platform. Here are some of the highlights:
- Azure Applied AI Services – Modernize business processes with task-specific AI, accelerate development with built-in business logic
- Azure Cognitive Services – Open up the ability to see, hear, speak, and understand user input and improve the capability of AI to make decisions in apps
- Azure Machine Learning – Access open frameworks and libraries to build, deploy, and manage AI models, with the ability to scale and innovate
- AI Infrastructure – Take an AI model from a few hundred test subjects to millions of users, with Azure’s infinitely scalable architecture
- Azure Automated Machine Learning – Build automated AI models through a no-code UI or through the SDK
- MLOps – Train reproducibility with an autoscaling compute, which allows advanced tracking of datasets, code, and experiments
- Cloud-scale Analytics – Embed enterprise-level analytics solutions into business intelligence, data warehouse, governance, and machine learning projects
- Business Intelligence – Self-service analytical and enterprise business intelligence tools are available on Azure, with Microsoft Teams and Office 365 built into the platform
- Knowledge Mining – Extract meaningful data from databases to provide deeper understanding, unveil insights, and find relationships and patterns in the data