data engineering

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

Alexander Blaufuss Blog, Data Science

Management Summary

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. [1] 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.

Machine Learning in the Cloud

The opportunities that can open up for companies following a successful migration to the cloud are illustrated, for example, by the broad range of Machine-Learning-as-a-Service (MLaaS) solutions. Standardized services, such as automated video and image recognition or translation from voice to text (and vice versa), significantly reduce the amount of expertise required to apply machine learning or AI. The previously high entry barrier to leveraging central business units through machine learning will continue to fall rapidly in the coming years, as all cloud service providers will offer this service. This will also enable companies that have not yet built up dedicated resources in the AI area to take advantage of the opportunities that arise from using AI for their products, services, and processes.

Provider for Cloud Computing

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. These can be supplemented by various third-party modules. 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.

What to expect from our whitepaper “Machine Learning in the Cloud”

The availability of local data centers and the associated infrastructure is no longer necessarily required to apply machine learning methods. In this white paper, we examine the offerings of the three largest cloud computing providers, AWS, Azure, and GCP, which lower the barriers to successful ML projects. Focusing on the required ML and IT competencies, we will look at the offerings and highlight the cloud providers’ differences.

Über den Autor

Alexander Blaufuss

I am a data scientist at STATWORX. Fascinated by the world of data and AI, I am interested in Deep Learning, communicating the insights gained from data and the techniques for visualizing data. If you have any questions about my Blog or Data Science in general, you are welcome to contact me via Email or LinkedIn.

ABOUT US


STATWORX
is a consulting company for data science, statistics, machine learning and artificial intelligence located in Frankfurt, Zurich and Vienna. Sign up for our NEWSLETTER and receive reads and treats from the world of data science and AI. If you have questions or suggestions, please write us an e-mail addressed to blog(at)statworx.com.