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Machine Learning in the Cloud – Comparing AWS, Azure, and GCP

  • Expert Alexander Blaufuss
  • Date 10. November 2020
  • Topic Machine Learning
  • Format whitepaper
  • Category Management
Machine Learning in the Cloud – Comparing AWS, Azure, and GCP

What’s it about?

In this white paper we provide you with an overview of the offerings of the three largest providers for cloud computing, AWS, Azure and GCP. We also look at how these providers lower the barriers to successful ML projects.

Digital transformation is a challenge for companies of all industries and sizes. Based on current surveys, data protection and security requirements and a lack of IT competence in companies have been identified as the greatest obstacles to successful digitization. For companies to continue to be successful in a digital, software, and data-driven age, the necessary technical requirements must be established. The use of cloud technology is seen as an essential element in this process. The migration of IT infrastructures or applications to the cloud is an important step to ensure the digital competitiveness of organizations further.

The three largest providers for cloud computing, Amazon Web Services (AWS), Microsoft Azure (Azure), and Google Cloud Platform (GCP), differ in their machine learning services only in certain areas. AWS relies on a broad range of its ML services, not least because of its market position. Microsoft Azure is perceived as a secure partner for companies that have relied on Microsoft products in the past. GCP can differentiate itself from the competition by offering specialized products in the area of Deep Learning. The white paper provides a detailed explanation of the similarities and differences of these cloud providers.


Alexander Blaufuss

Team AI Academy

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