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    The current excitement surrounding artificial intelligence (AI) would not be possible without the use of snowcloud computing. The development of new “intelligent” products, services, and business models is only made possible by the widespread availability of cloud-based artificial intelligence (AI) services (machine learning, etc.) and the requisite and readily available computing capacity. At the same time, AI services make it possible for public cloud providers like Amazon Web Services, Microsoft, and Google to expand their businesses. As a result, one can see evidence of an “interdependency between the cloud and AI.”

    After more than ten years, cloud computing has matured into a lucrative business that can be pursued by service providers like Amazon Web Services or Microsoft. On the other hand, laggards like Google and Alibaba are increasing their competitiveness in the market. And with the enormous and ongoing launch of AI-related cloud services, providers have increased the competitive pressure on themselves in order to boost their attractiveness among their client base in order to attract more customers.


    The Cloud Is Behind Ai, And Ai Is Behind The Snowcloud

    Building artificial intelligence (AI) systems that are both powerful and highly scalable is an expensive endeavor for businesses of any size. In the end, the process of training algorithms and operating the analytics systems that correspond to those algorithms require a massive amount of processing power. It is impossible to provide the necessary processing power in the exact amount and on time using one’s own basement, server room, or data center as the location of the operation. Power devoted to computing that is thereafter not required any longer.

    When one investigates the realms of Amazon, Microsoft, or Google, one finds that all three service providers have amassed a great amount of processing power in recent years and all three equally possess a significant portion of the cloud computing market, which is worth 40 billion USD. The next natural step for all of them in the cloud is to broaden their service offerings to include artificial intelligence-related options.

    On the one hand, having simple access to computing power, data, connectivity, and additive platform services is necessary for developing AI applications and intelligently enhancing already existing apps. In the alternative, it is vital to achieving attraction among already existing clients as well as to win new customers. Both are looking for solutions that can easily be implemented into their applications and business models to make AI more useful.


    The Amazon Web Services Company

    Amazon Web Services (AWS), in addition to being the company that invented the cloud and leading the way in terms of innovation, is also the one that continues to dominate the public cloud market worldwide. As of right now, the most popular cloud environment for creating and deploying cloud and AI applications is Amazon Web Services (AWS). This is owing to AWS’s scalability as well as its complete collection of platform services. At the most recent re: Invent conference, AWS made a number of announcements, one of which was the presentation of Amazon Cloud 9, which was the result of the company’s acquisition of snowcloud IDE Inc.

    In July 2016. A development environment that is hosted in the cloud and is directly connected to the AWS cloud platform. This environment is used to create cloud-native apps. In addition, Amazon Web Services (AWS) has introduced six new machine learning as a service (MLaaS) offerings, which include a translation service, natural language processing (NLP) service, and video analysis service. In addition, AWS provides strong services for the development of artificial intelligence applications such as MXNet, Lex, Rekognition, and SageMaker. Particularly noteworthy is SageMaker due to the fact that it facilitates greater control over the machine learning application development lifecycle.

    However, the lock-in strategy is one that AWS takes with AI-related services as well, just as it does with all of its cloud services. After the development of an AI solution, AWS will continue to serve as the primary operating platform because all AI services have been tightly integrated into the environment provided by AWS.

    Amazon continues to implement its tried and tested business approach. Following Amazon’s decision to make the technologies that power its massively scalable eCommerce platform available to the public as a service via AWS, other technologies, such as those that power Alexa, have since been developed to assist customers in integrating their own chatbots or voice assistants into their applications.



    Within the context of the commercial world, Microsoft has access to a big customer base. This, together with having a comprehensive portfolio of snowcloud and AI services, provides generally solid preconditions for also establishing oneself as a top AI market player. Microsoft may be high on the priority list of enterprise customers particularly due to the company’s broad offering of productivity and business process solutions.

    With products such as Windows, Office 365, and Dynamics 365, Microsoft maintains a strong presence in the middle of the digital ecosystems of businesses all over the world. And this is precisely the moment where the data exist in the form of dataflows that can be utilized to train machine learning algorithms and construct neural networks. A company’s AI plan may be put into action by utilizing the cloud-based AI services that are made available through Microsoft Azure, which serves as the primary hub where everything works together.


    Google Snow Cloud Bachelor Gulch

    Google is still lagging behind Amazon Web Services (AWS) and Microsoft in the snowcloud. However, AI has the potential to completely transform the game. When compared to AWS and Microsoft, the current portfolio of artificial intelligence services offered by Google reveals that the company is, by far, the least inventive of the innovative providers of public cloud and AI services.

    This is incredible when you realize that Google has spent a total of USD 3.9 billion on AI research and development so far. When compared to its rivals, Amazon’s investment of USD 871 million is significantly higher than Microsoft’s investment of USD 690 million. Simply put, Google is not very consistent in its execution.

    But! Already, Google has more than one million users of artificial intelligence (AI), primarily as a result of its purchase of the data science community “Kaggle,” and it possesses a significant amount of AI know-how (primarily as a result of its purchase of “DeepMind”). Additionally, among software developers, Google is regarded as the most robust AI platform available, boasting the most cutting-edge AI technologies. In addition, TensorFlow is the preeminent artificial intelligence (AI) engine as well as the most essential AI platform for developers, and it serves as the basis for a wide variety of AI projects.

    In addition, Google has designed its very own Tensor Processing Units (TPUs), which have been modified in such a way as to be compatible exclusively with TensorFlow. A new machine learning as a service (MLaaS) designed to assist in the development of deep learning models was introduced not too long ago by Google under the brand name Cloud AutoML.

    The potential of artificial intelligence services that are hosted on the Google snowcloud Platform is readily apparent when one considers the areas in which Google, through its Android OS, already has a foothold (for example, smartphones, home appliances, smart homes, or automobiles). The one and only negative aspect is that Google may still only provide services to software developers. The access to enterprise clients that can break ties between competitors, which is something that Microsoft owns, is currently lacking.


    The Application Of Ai Will Completely Transform The Public Snowcloud

    The industry for AI-based platforms and services is still in its infancy at this point. However, in response to the growing demand placed on businesses to provide their clients with intelligent md cloud goods and services, these organisations are likely to continue their quest for the appropriate technology and support. In addition, it is a well-known truth that fresh “intelligent” goods, services, and business models cannot be developed without uncomplicated access to cloud-based artificial intelligence (AI) services, in addition to the essential and quickly accessible computer capacity.

    As a consequence of this, it is not practical for businesses to develop their own AI systems because it is practically impossible to run such systems in a way that is both efficient and scalable. In addition, it is essential not to minimize the significance of having access to devices and data that are located all over the world and that need to be studied. Cloud platforms that are both scalable on a global scale and properly connected are the only ones capable of accomplishing this goal.

    AI has the potential to become a game-changing technology for providers operating in the public cloud. After Amazon Web Services and Microsoft took the lead, Google was unable to make considerable headway in its pursuit of the pack. Nevertheless, the AI portfolio that Google has potentially makes a difference.

    TensorFlow, in particular, and the popularity it has among developers might work to Google’s advantage. On the other hand, AWS and Microsoft are aware of it and are working jointly to combat it. Both firms collaborated to build “Gluon,” an open-source deep-learning package that has a very familiar appearance and functions quite similarly to TensorFlow. In addition, Amazon Web Services (AWS) and Microsoft offer a wide variety of artificial intelligence engines, or frameworks, rather than just TensorFlow.

    It is quite unlikely that Google’s AI services will be sufficient for the company to catch up to AWS. However, Microsoft was able to swiftly sense the presence of the rival. For Microsoft, the rate at which the provider can persuade a business customer of the value of its artificial intelligence service portfolio is of the utmost importance.

    And simultaneously, emphasize how vital other Microsoft technologies, such as Azure IoT, are, and examine how to include them into an AI plan. AWS is not going to deviate from its dual strategy and will continue to focus on both commercial clients and developers. As a result, the company will maintain its position as the leader in the public snowcloud market. AWS will become the go-to destination for cloud-native AI customers as well as everyone else who does not wish to use TensorFlow in any capacity whatsoever. And certainly not to be overlooked is the vast client base that is open to new ideas and is well aware of the advantages offered by AI services.

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