Today, without snowcloud computing, there would be no artificial intelligence (AI) hype. Only easy access to modern cloud-based AI services (machine learning, etc.) and the necessary and rapidly available computing power allow the development of new “smart” products, services, and business models. At the same time, AI services ensure the growth of public cloud providers such as Amazon Web Services, Microsoft, and Google. Therefore, the “interdependence between the cloud and AI” can be observed.
Over 10 years later, cloud computing has become a thriving business for providers such as Amazon Web Services or Microsoft. However, competition from latecomers such as Google and Alibaba is growing. And with the continued massive introduction of AI-related cloud services, vendors themselves have increased the competitive pressure to increase their appeal to their customers.
Cloud Supports AI And Vice Versa
Building and running a powerful and highly scalable AI system is a costly affair for companies of any size. Finally, training algorithms and running the relevant analytics system again requires heaps of computing power. It is impossible to provide the required amount of computing power in the right amount and on time through your own basement, server room, or data center. Computing power that is no longer needed.
Take a look at the sectors of Amazon, Microsoft, and Google snowcloud, all three providers have accumulated a huge amount of computing power in recent years and equally own a large part of the 40 billion cloud computing industry. For all of them, expanding your portfolio with AI services is a logical next step in the cloud. On the one hand, the development of AI applications, respectively, the intelligent improvement of existing applications requires easy access to computing power, data, connectivity, and additional platform services. Otherwise, it is necessary to gain appeal among existing customers and win new customers. Both are looking for affordable solutions to integrate AI into their applications and business models.
Amazon Web Services
Amazon Web Services (AWS) is not only the pioneer and innovator of the cloud but also the global public cloud market leader. Currently, AWS is an important cloud environment for developing and deploying AI and cloud applications, due to its scalability and comprehensive set of platform services. Among other announcements, AWS unveiled at the recent re: Invent summit of Amazon Cloud 9 (acquisition of Cloud9 IDE Inc. in July 2016). A cloud-based development environment that integrates directly with the AWS cloud platform to develop cloud-native applications. In addition, AWS announced six machine learning services as MLaaS services, including a video analytics service as well as an NLP service, and a translation service. In addition, AWS offers powerful services for MXNet, Lex, Recognition, and SageMaker, AI application development. SageMaker, in particular, stands out because it helps control the entire lifecycle of machine learning applications.
However, like all cloud services, AWS also adopts a lockdown approach with AI-related services. All AI services are closely related to the AWS environment to ensure that AWS remains the operating platform once the AI solution is ready.
Amazon is also based on its successful strategy. After Amazon made the technologies behind its massively expandable e-commerce platform publicly available through AWS as a service, the technologies behind Alexa, for example, allow consumers to create their own Help chatbots or assistants to voice your applications.
Microsoft has access to a wide customer base in the business environment. It, with an extensive portfolio of cloud and AI services, offers good prerequisites to establish itself primarily as a leading player in the AI market. Microsoft Enterprise can be high on the consumer agenda, especially due to its comprehensive offer of productivity and business process solutions.
Microsoft enters the digital ecosystem of companies around the world with products such as Windows, Office 365 and Dynamics 365. And this is exactly where the data is, respectively, the flow of data that can be used to train learning algorithms. Automated indexing and construction of neural networks. . Microsoft Azure is the hub where everything works together and provides the cloud-based AI services needed to put the company’s AI strategy into practice.
In the snowcloud, Google lags behind AWS and Microsoft. However, AI can be a game-changer. When you compare Google’s existing portfolio of AI services with AWS and Microsoft, you can see that Google is clearly behind modern AI and public cloud service providers. It’s amazing when you consider that Google has so far invested $ 3.9 billion in AI. In comparison, Amazon has invested USD 871 million and Microsoft only USD 690 million. Google lacks consistent execution.
But! Google already has over 1 million AI users (primarily through its acquisition of the “Kaggle” data science community) S has a lot of information. In addition, among the developers, Google is considered to be the most powerful AI platform with the latest AI tools. Furthermore, TensorFlow is a leading AI engine and, for developers, the most important AI platform, serving as the basis for numerous AI projects. In addition, Google has developed its own Tensor Processing Units (TPUs) specifically designed for use with TensorFlow. Google recently announced Cloud AutoML, an MLaaS that targets inexperienced machine learning developers to help build in-depth learning models.
And when you consider where Google is through the Android operating system (for example, smartphones, appliances, smart homes, or cars), the potential of AI services running on the Google snowcloud Platform becomes clear. ۔ The only downside is that Google can still only cater to developers. Tiebreaker access to enterprise users is still missing, something owned by Microsoft.
AI Becomes A Tipping Point In The Public Cloud
The market for AI platforms and services is still in its infancy. But with the growing demand for customer service with intelligent products and services. Companies will continue to seek the necessary technologies and support. And it is a fact that only easy access to cloud-based artificial intelligence services. As well as the necessary and quickly accessible computing power. Are needed to develop new “smart” products, services, and business models. Therefore, it does not make sense for companies to build in-house AI systems. As it is almost impossible to operate them efficiently and scalably. Furthermore, it is important not to underestimate the access to globally distributed devices and data that needs to be analyzed. Only well-connected, globally expanding cloud platforms can achieve this.
For providers, AI can be a game-changer in the public cloud. After AWS and Microsoft began to lead the pack, Google was unable to catch up in any significant way. However, Google’s snow beaver creek portfolio can make a difference. TensorFlow, in particular, and its popularity with developers could go hand in hand with Google. But Friday night funkin unblocked games 911 and Microsoft are vigilant and work together against it. Glon is an open-source deep-learning library developed jointly by the two companies, which is very similar to TensorFlow. Also, AWS and Microsoft offer a wide range of AI engines (frameworks) instead of just TensorFlow.
It is doubtful that AI services will be enough for Google to catch AWS. But Microsoft was able to feel the competition faster. What matters to Microsoft is how quickly the vendor can convince its business to use its portfolio of AI services. At the same time, be aware of the importance of other Microsoft products (eg Azure IoT). And consider them for AI strategy. AWS will stick to its dual strategy, focusing on both developers and enterprise users. And continue to lead the public snowcloud market. AWS will be home to everyone who just doesn’t want to take advantage of TensorFlow. Especially local AI users in the cloud. And don’t forget the large customer base that is innovative and knows the benefits of AI services.
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