The field of data science is varied, and today there are many different roles and responsibilities involved in the process. Data science work typically involves working with unstructured data, implementing machine learning (ML) concepts and techniques, generating insights. This process typically ends in a visual presentation of data-driven insights.

Machine learning is a critical element of the process, but training ML models is often a time-consuming process that requires a lot of resources. In the past, gaining access to ML resources was difficult and expensive. Today, many cloud computing vendors offer resources for data science in the cloud.

This article reviews the machine learning options on AWS, Azure and FCP to help you decide which resource meets your ML needs.

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