Microsoft Azure has been taking the world by storm, and rightly so, considering that it’s not just your typical cloud storage platform. Rather, you can think of it as a framework for rethinking the way enterprise architectures are built and applications are designed. In a way, it gives enterprises all the different components needed to leverage cloud computing as a whole.
In fact, a study by Spiceworks shows that Microsoft Azure is the most popular Infrastructure as a Service (IaaS), and has grown at a phenomenal rate over the last few years. Another report by Forbes states that Azure is the only major platform that’s been ranked as a leader consistently in IaaS, PaaS, and SaaS platforms.
That’s just to give you a peek into its popularity.
You may wonder what’s the big deal about Azure? Why is it so popular? Well, the simple answer is Azure is way more than what meets the eye.
It’s not your typical cloud computing platform because Microsoft is working on it all the time to improve its offerings and to extend its arm to cover every possible domain.
For example, in October 2016, Microsoft CEO Satya Nadella announced at an event in Dublin that Azure cloud will become the first artificial intelligence supercomputer in the world.
This announcement created a wave of excitement in the cloud community because AI is really the future.
But what did Nadella really mean in that announcement? How will Azure transform to an AI supercomputer?
To clarify these hundreds of doubts, Microsoft posted an explanation on its company website, according to which, Azure will bring in a set of apps based on AI technologies. The company believes that AI and machine learning are likely to define the next generation of apps powered by the cloud, and it wants to lead the way.
Following this announcement, Microsoft has been taking many steps to transform Azure into an AI supercomputer. Here’s a look at what Microsoft has done so far in this ambitious effort.
The first step to implementing an idea is to form a solid team, and no one knows it better than Microsoft.
In October 2016, Microsoft formed an AI team. It is headed by Harry Shum, a 20-year old in the company who is known for his work in Cortana intelligence and Bing search. He will be assisted by more than 5,000 engineers and scientists who work in Microsoft’s AI and Research Group.
GPUs and FPGAs
Gone are the days when CPUs were enough to power computers. With the growing computing power and data volumes, we need something more than CPUs, and this is where GPUs and FGPAs come into place.
Graphics Processing Units (GPUs) are powerful and programmable computing units that work in tandem with CPUs to enhance the processing capabilities of machines. Earlier, GPUs were mostly used for 3D game rendering, but that’s changing, as companies realize their ability to handle more computational workloads. In this sense, GPU is a computational powerhouse.
To make the most of this resource, Microsoft is planning to build cloud processing power based on GPUs to meet the needs of the next generation of applications. In fact, the company plans to scale GPUs so they can process tasks in parallel. This will be ideal for high workloads.
Along with GPUs, Microsoft also plans to expand the use of what’s called the Field Programmable Gate Arrays (FGPAs). At a high level, FGPAs allow developers to write all kinds of neural network code, spread it across multiple FGPAs, and run all of it at the speed of a silicon chip. To top it, FGPAs can be reprogrammed within seconds to respond to changes in artificial intelligence software or, for that matter, to even meet any unexpected event.
Both GPUs and FGPAs are most conducive for AI applications because they have the computing power, speed and workload-handling capability – things that are going to be an integral part of the next generation of AI applications.
Already, Microsoft has started implementing some of these technologies in Azure. For example, it’s using the combined power of FGPA and GPU to create a virtual machine on Azure that can power 25GB per second with 10-times less latency. Imagine the speed of search with this computing power!
That’s just the beginning. As Microsoft does more research in this area, the speeds are going to increase and latency rates are going to reduce, thereby making it more conducive for AI applications.
GPUs and FGPAs are the computing bed, but that’s not all. You need higher level services to build AI applications, and that needs APIs.
These APIs help to perform natural language processing, integrate speech recognition, enhance knowledge exploration, improve search, and more.
Here’s a brief look into the APIs developed by Microsoft for boosting AI applications.
• Computer vision API – This API allows you to analyze an image to identify content, tag, label, and create all kinds of domain-specific models.
• Content moderator API – This API will allow machine-assisted moderation of content, images, and videos. In addition, it’ll augment human review by providing machine learning models.
• Emotion API – This API detects the motions on a person’s face to identify the moods and feelings, so responses can be personalized accordingly.
• Face API – This API helps to detect human faces, compare similar ones, and organize them into groups based on facial similarities. You can use this along with emotion API to provide the best possible response to every customer.
• Video API – With this API, you can process videos intelligently to analyze faces and images, smoothen videos, and do so much more.
You can use these APIs by themselves or in combination with others to build and provide advanced features in your application. These can come particularly handy when you want to create AI-based applications on Azure.
With these strategies in place, Microsoft is all set to advance its ambitions of making Azure the first AI supercomputer. However, it faces intense competition from rivals like IBM, AWS, and Google.
Already, IBM has started offering its AI engine, Watson, as a service. It’s also planning to combine Watson with other services such as IBM Data Science Experience to create advanced products that’ll give its clients more capabilities than ever before.
Likewise, Google has been working on advanced machine learning plans to bring in more customers to the Google Cloud Platform. It is also looking to tap into an open-source library called Tensorflow to further its AI plans.
Amazon is not to be left behind, too. A few months back, it unveiled a plan for its GPU-powered cloud computing service to offer AI-based services, genomics, molecular modeling, and more.
In the light of this competition, it’s important for Microsoft to move ahead faster with its plan to get a big slice of the AI market before others start closing in.
Nadella announced that the company is planning to make the Azure platform as the first AI supercomputer in the world. Over the last few months, a lot of progress has happened in this area that includes the use of FGPAs and GPUs for computing and the development of APIs for communicating with AI applications.
Though there’s more work needed in this area, Microsoft is nevertheless on the right path to fulfilling its ambitious plans.
Exciting days are ahead for both Microsoft and for the tech industry as a whole.