Vol. 1 No. 2 (2021): Journal of AI-Assisted Scientific Discovery
Articles

The AI Cloud Race: How AWS, Google, and Azure Are Competing for AI Dominance

Naresh Dulam
Vice President Sr Lead Software Engineer, JP Morgan Chase, USA
Babulal Shaik
Cloud Solutions Architect, Amazon Web Services, USA
Abhilash Katari
Engineering Lead, Persistent Systems Inc, USA
COver

Published 09-12-2021

Keywords

  • AI cloud,
  • Google Cloud

How to Cite

[1]
Naresh Dulam, Babulal Shaik, and Abhilash Katari, “The AI Cloud Race: How AWS, Google, and Azure Are Competing for AI Dominance ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, pp. 304–328, Dec. 2021, Accessed: Dec. 23, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/227

Abstract

The race for AI dominance in cloud computing was heating up. Amazon Web Services (AWS), Google Cloud, and Microsoft Azure positioned themselves as the key players in the evolving artificial intelligence (AI) landscape. As the leader in cloud services, AWS built a comprehensive portfolio aimed at developers, data scientists, and enterprises, with offerings like SageMaker for building, training, and deploying machine learning models and a suite of other AI tools such as Deep Learning AMIs. AWS'sAWS's early focus on scalability and flexibility made it an appealing choice for organizations looking to integrate AI into their operations quickly. Google, a company known for its deep AI expertise, leaned into its strength in research and machine learning technologies, offering TensorFlow, the most widely used deep learning framework, and other services like AutoML and Google AI Platform, designed to simplify the process of developing and deploying AI models, from data preparation to model training. Google's emphasis on cutting-edge AI research and its strong performance in natural language processing and vision-based AI applications made it the preferred choice for businesses and developers focused on innovation. On the other hand, Microsoft Azure focused heavily on integrating AI with its existing enterprise software products, like Office 365 and Dynamics, to appeal to a broader base of enterprise customers. With Azure Machine Learning, Microsoft created a unified platform for managing the end-to-end lifecycle of machine learning models, offering robust tools for model development, deployment, and monitoring. Azure's deep connections with large enterprises and strong support for hybrid cloud models allowed businesses to integrate AI into their on-premises environments, giving it a unique edge in the AI race. In this competitive environment, each cloud provider sought to carve out a niche by offering differentiated value: AWS capitalized on its scale and breadth of services; Google leveraged its AI-first approach, founded on its research prowess; and Microsoft integrated AI with its established enterprise ecosystem. As the demand for AI-powered services surged, these companies relentlessly advanced their offerings, each jockeying for position as the go-to platform for AI development in the cloud, setting the stage for the continued evolution of AI technology.

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References

  1. Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73-80.
  2. Kumar, Y., Kaul, S., & Sood, K. (2019, February). Effective use of the machine learning approaches on different clouds. In Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India.
  3. West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems. AI Now, 1-33.
  4. Markoff, J., & Lohr, S. (2016). The race is on to control artificial intelligence; Tech companies compete for talent, publicity and the industry's future. International New York Times, NA-NA.
  5. Crawford, K., & Joler, V. (2018). Anatomy of an AI System. Anatomy of an AI System.
  6. Lauterbach, A., & Bonime-Blanc, A. (2018). The artificial intelligence imperative: A practical roadmap for business. Bloomsbury Publishing USA.
  7. Markoff, J., & Lohr, S. (2016). Race is on to control artificial intelligence; Tech giants like Amazon and Google are jockeying to win the'platform war'. International New York Times, NA-NA.
  8. Jennings, C. (2019). Artificial Intelligence: rise of the lightspeed learners. Rowman & Littlefield.
  9. Vempati, S. S. (2016). India and the artificial intelligence revolution (Vol. 1). Washington, DC: Carnegie Endowment for International Peace.
  10. Rock, D. (2019). Engineering value: The returns to technological talent and investments in artificial intelligence. Available at SSRN 3427412.
  11. Rosenthal, A., Mork, P., Li, M. H., Stanford, J., Koester, D., & Reynolds, P. (2010). Cloud computing: a new business paradigm for biomedical information sharing. Journal of biomedical informatics, 43(2), 342-353.
  12. Magoulès, F., Pan, J., & Teng, F. (2012). Cloud computing: Data-intensive computing and scheduling. CRC press.
  13. Dunie, R., Schulte, W. R., Cantara, M., & Kerremans, M. (2015). Magic Quadrant for intelligent business process management suites. Gartner Inc.
  14. Haxhixhemajli, D. (2012). Visibility Aspects Importance of User Interface Reception in Cloud Computing Applications with Increased Automation.
  15. Choo, R., & Ko, R. (2015). The cloud security ecosystem: technical, legal, business and management issues. Syngress.
  16. Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
  17. Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).
  18. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
  19. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.