The cloud space is constantly evolving, which in turn offers tremendous opportunities for corporations wanting to establish themselves as cloud computing leaders. The cloud market is expected to grow more than double within three years, to $195 billion by the end of 2020, according to a report. This particular domain was simply known as the "cloud" a decade ago, comprising infrastructure-as-a-service for virtualized workloads, but with the fractalization of offerings, businesses now need to be more specific about what aspect of the cloud they are dealing with.
One of the cloud computing's most critical divisions is artificial intelligence, which includes machine learning(ML) and deep learning, as we believe it will affect almost any market result more than any other array of innovations in recent history.
And to evaluate companies committed in this space, Gartner has developed it's AI Developer Services' report, which concentrates on the platforms that deliver AI services through APIs. Aible, AWS, Google Cloud, H2O.ai, IBM Cloud, Microsoft Azure, Prevision.io, Salesforce, SAP and Tencent were the corporations involved in the study; however, Alibaba and Baidu were omitted for this review.
It is pretty obvious that if you think of cloud computing, you would probably think of Microsoft Azure, Google Cloud Platform(GCP), or the IBM Cloud would be the top performers in terms of AI Developer Services.
Curiously, the study analyzes a total of 10 vendors offering cloud-hosted AI services to app developers, with an emphasis on vendors providing services in the fields of automated machine learning, natural language, and visual recognition and analysis.
Indeed, Gartner's Magic Quadrant shows that in terms of both vision and ability to implement, AWS is the highest scoring performer. Sure, let that soak in for a second, Gartner says Google, IBM and Microsoft are underperforming than AWS AI Developer Services. We know that's going to shock everyone, but we think it's worth it because we've been watching AWS closely for many years and thought this day would come, even if it's earlier than we'd expected.
According to Gartner, Amazon cloud's "extensively broad and capable portfolio" is one of its biggest strengths combined with its mammoth market reach which accounts for almost one-third of the global cloud market.
AWS was also praised for its broad portfolio of services which include SageMaker AutoPilot, launched last year which is capable of generating automated machine-learning models.
One of the cloud computing's most critical divisions is artificial intelligence, which includes machine learning(ML) and deep learning, as we believe it will affect almost any market result more than any other array of innovations in recent history.
And to evaluate companies committed in this space, Gartner has developed it's AI Developer Services' report, which concentrates on the platforms that deliver AI services through APIs. Aible, AWS, Google Cloud, H2O.ai, IBM Cloud, Microsoft Azure, Prevision.io, Salesforce, SAP and Tencent were the corporations involved in the study; however, Alibaba and Baidu were omitted for this review.
It is pretty obvious that if you think of cloud computing, you would probably think of Microsoft Azure, Google Cloud Platform(GCP), or the IBM Cloud would be the top performers in terms of AI Developer Services.
Curiously, the study analyzes a total of 10 vendors offering cloud-hosted AI services to app developers, with an emphasis on vendors providing services in the fields of automated machine learning, natural language, and visual recognition and analysis.
Indeed, Gartner's Magic Quadrant shows that in terms of both vision and ability to implement, AWS is the highest scoring performer. Sure, let that soak in for a second, Gartner says Google, IBM and Microsoft are underperforming than AWS AI Developer Services. We know that's going to shock everyone, but we think it's worth it because we've been watching AWS closely for many years and thought this day would come, even if it's earlier than we'd expected.
According to Gartner, Amazon cloud's "extensively broad and capable portfolio" is one of its biggest strengths combined with its mammoth market reach which accounts for almost one-third of the global cloud market.
AWS was also praised for its broad portfolio of services which include SageMaker AutoPilot, launched last year which is capable of generating automated machine-learning models.
How AWS dominated in the Cloud AI Dev Services?
Amazon's cloud AI space success was entirely predictable where it dominated 33.8 percent of the market while competitors like Microsoft, Google, and IBM together represent 30.8 percent of the total sector. AWS was known in cloud AI space for its broadest and most in-depth service capabilities. The report concluded that offerings from AWS could meet developers ' needs across many skill levels, from those with little AI/machine learning experience to more advanced ones. AWS has launched several products in recent years aimed at democratizing AI and machine learning.
AWS services meet the needs of those without ML expertise as well as those looking for advanced functionality. For those who do not have ML expertise can use pre-trained AI tools with constantly improving APIs, and those who are searching for more advanced functions may implement the more robust machine learning framework. The company has even launched the' Plug and Play' AI solutions— Contact Lens for Amazon Connect and Amazon Kendra, generating better insight from business data for those with no previous ML experience. This move has helped other companies, as there is a significant lack of expertise in qualified and experienced machine learning and data science experts.
A Deeper Dive into Amazon AWS
Let's begin from the bottom layer, to look a little deeper at the technology stack offered by AWS. Here, AWS offers services dedicated to developers with experience in machine learning and data science. This covers the most common architectures and infrastructures, a variety of ways to train and infer models including CPUs and GPUs, custom ASICs, FPGAs, and even elastic inference capabilities. The business does just that.
The middle layer called Machine Learning Services by AWS is all about SageMaker. SageMaker Studio, introduced last year is an End-to-End Integrated Development Environment (IDE) that manages the ML workflow from data planning to train the system to manage and share those elements with other developers, hosting those models, and distributing and evaluating their performance. This layer is extensively directed for those folks who do not have the fundamental experience of machine learning but who possess prior data experience.
For the peak layer, AWS has AI services that come in as the biggest surprise to those users who only rabbit-hole AWS as a service in the network bucket. It packs some amazing APIs for vision, voice, text search, chatbots, and even going up one level, to abilities that include personalization, forecasting, and fraud detection (all focused on Amazon's own internally developed capabilities), they have recently announced Contact Lens that offers ML-powered Contact Center analytics for Amazon Connect.
According to a Synergy Research Group's chief analyst, John Dinsdal, "AWS was essentially the first to launch and make its own market before other business giants launched similar cloud services. Though companies like Microsoft and Google have experimented with cloud services, they have failed to properly use it in the process of project development."
Summary of Gartner's Magic Quadrant
Google, on the other end, initially claimed to be the cloud leader in machine learning and was one of the first to deliver AI and AutoML tools to developers and companies, but was ranked only ahead of Microsoft in terms of vision, but fractionally lagging behind in implementing capabilities. The study evaluated Google for its robust language resources and its AutoML tool. One thing discussed in the study was the excellent image recognition service provided by Google, along with the lack of maturity in their cloud space, and less cloud infrastructure standing in competition with AWS and Azure.
Sitting between AWS and Google, Microsoft has gained evaluations of its high-level investment in AI, along with the versatility of deployment of its AI services and its wide range of supported languages. The research, however, pointed to the lack of the NLG (Natural Language Generation) services. According to Gartner, Microsoft also has a complicated naming plan spanning multiple lines of business such as Azure’s cognitive services, Cortana Services, and more.
In reality, because of its investments in Watson and augmented AI and other robust AI and ML offerings, IBM has found a place in Gartner's leadership quadrant, but the position is a little behind the other three above-mentioned tech giants. According to Gartner, while IBM provides a wide variety of AI facilities, its architecture makes the role of converging them all together in a well-integrated ecosystem which turns out to be more complicated.