How AWS Nudged Out IBM, Google & Microsoft From The Cloud AI Space

How AWS Nudged Out IBM, Google & Microsoft From The Cloud AI Space

The cloud space is ever-evolving, which in turn offers incredible opportunities for companies wishing to establish themselves as leaders in cloud computing. According to a report, the cloud market is expected to grow more than double in three years, to $195 billion by the end of 2020. A decade ago, the space was simply known as “cloud” comprising infrastructure-as-a-service for virtualised workloads, however, with the fractalisation of offerings, companies now need to be more specific in terms of what aspect of cloud they are dealing with.

In the recent era, artificial intelligence has been considered an important segment of the cloud space, which involves machine learning and deep learning. And, to analyse companies engaged in this space, Gartner, has come up with their report on the ‘AI Developer Services‘, which focuses on the platforms that deliver AI services via APIs. The companies involved in the study were Aible, AWS, Google,, IBM, Microsoft,, Salesforce, SAP and Tencent; however, Alibaba and Baidu were excluded for this analysis.

According to the report, AWS has been considered as the highest-ranked leader in terms of both vision and ability to execute, where it outperformed Google, IBM, and Microsoft for AI Developer Services. The firm was praised for its wide range of services, including SageMaker AutoPilot, announced late last year, which automatically generates machine-learning models. However, some shortcomings in SageMaker were also addressed in the report. 

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How Is AWS Still Leading The Cloud AI Dev Services?

Amazon’s success in the cloud AI space was entirely predictable, where it has been dominating 33.8% of the market while competitors like Microsoft, Google, and IBM together account for 30.8% of the total market. AWS has been known for its broadest and the most in-depth service capabilities, in the cloud AI space. The report noted that AWS offerings could support the needs of developers across many skill levels, from those with little AI/machine learning experience to those who are more advanced. In recent years, AWS has launched multiple products aimed at democratising AI and machine learning. It also offers courseware and consulting services for developers and organisations looking for more expert guidance on machine learning projects.

AWS’s offer for developers caters to the needs both of those without ML skills and those seeking advanced functions. Those without ML skills can use pre-trained AI services with continuously learning APIs, and those looking for more advanced functions can deploy AWS’ fuller-featured machine learning platform. The firm has even introduced its ‘Plug and Play’ AI tools — Contact Lens for Amazon Connect and Amazon Kendra, for those with no prior experience of ML gain better insight from business data. This move has been beneficial to many companies as there is a considerable shortage of qualified and experienced machine learning and data science experts.

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With AWS, users can choose from pre-trained AI services for computer vision, language, recommendations, and forecasting to build, train, and deploy machine learning models at scale. Some of the tools that facilitate AI-based services include — Amazon CodeGuru, Amazon Fraud Detector, Amazon Lex, Amazon Polly, Amazon Textract, and Amazon Comprehend.

According to a chief analyst of Synergy Research Group, John Dinsdal, “AWS was essentially the first to launch and made the market it’s own before other giant companies launched similar cloud services. Although companies, such as Microsoft and Google, experimented in cloud technology, they failed to use it properly in project development.”

Other Leaders Of Gartner’s Magic Quadrant

Google, on the other hand, has previously claimed itself as the cloud leader in machine learning and was one of the first to offer AI and AutoML services to developers and businesses, however, has been ranked just ahead of Microsoft in terms of their vision but fractionally lagging in the ability to execute. The report appraised Google for its robust language services as well as their AutoML tool. Another aspect that was covered in the report was Google’s brilliant image recognition service, along with the lack of maturity in their cloud space, and lesser standing in cloud infrastructure compared with AWS and Azure. 

According to the Gartner analysts, Thomas Kurian’s leadership has attracted a positive tone for the company, however, the organisation is still undergoing substantial change and the full impact of which will not be apparent for some time.

Microsoft, sitting in between AWS and Google, won appraisals on its high-level investment in AI, along with the deployment flexibility of its AI services and its wide selection of supported languages. However, the analysis pointed out the lack of NLG (Natural Language Generation) services. Also, according to Gartner, Microsoft has a confusing branding strategy spanning multiple business units, such as Azure’s cognitive services, Cortana Services, and more.

How IBM Started To Lag The Cloud War?

IBM, indeed, found a place in the Gartner’s leadership quadrant, because of its investments in Watson and augmented AI and other robust AI and ML services, however, the place is a little behind the other three above mentioned tech giants. According to Gartner, although IBM comprises a wide variety of AI services, its framework complicates its task of converging them all together in a well-integrated environment.

For a company that was supposed to be the first tech giant to start working on AI, IBM’s performance in the last decade has been underwhelming. The firm took a slow take-off on its AI business, where other giants took it up as a challenge to build up their position actively. The firm heavily promoted their effort on building the technology, Watson, and invested billions, especially for the healthcare sector, however, the progress was slow because of the irregular implementation and difficulty of acquiring and analysing healthcare records. And therefore people, nowadays, are dismissive of Watson, which in turn affects the firm’s business model.

The glory days are gone, or so it seems. Another milestone that has been strongly criticised by analysts is its acquisition of open-source company — Red Hat. Although it was one of the big moves in the open-source community, IBM couldn’t deliver its promise of completely switching to cloud offerings. While revenue from Red Hat was up 24% in Q4 2019, IBM’s global technology services division was down 5% YoY. And, therefore, IBM decided to revamp its entire business model, which was built over decades. 

For this reinvention, Arvind Krishna, the new CEO from their cloud division was a critical aspect. As the head of the company’s cloud and cognitive software unit, Krishna’s leadership strongly suggests where the board of the company will be placing its bets, especially since competition like AWS have rocketed ahead. Although the cloud has been introduced to IBM, applying the vision entirely in IBM has been a difficult task. To succeed in the competitive space of cloud AI, IBM needs to revamp its business model by offering innovative services and should come up with bright new ideas and some brave decision making.

So, does this mean that IBM should scrap its low-marginal conventional divisions to focus on emerging technology? Or should the firm include modernisation of some of its business areas? There are many such decisions that IBM needs to make on an urgent basis, to survive in the cloud AI space.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Sejuti Das
Sejuti currently works as Associate Editor at Analytics India Magazine (AIM). Reach out at

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