Amazon Web Services (AWS) is a cloud platform from Amazon. It offers a wide range of different cloud-based services such as storage, databases, analytics, AI, development tools and more. Compared to its competitors, Microsoft Azure and Google Cloud, we consider AWS the most mature platform. Especially when it comes to bringing Machine Learning models to production.
The benefits of a cloud solution include:
AWS has a pay-as-you-go pricing model which means you only pay for the number of resources you need. Moreover, it is not necessary to have any long-term commitment, minimum upfront or spend investment.
Users can access AWS’s application hosting platform using the AWS Management Console or well-documented web services APIs, which makes it easy to use for either a new applicant or an existing applicant.
- Agility and Flexibility
AWS enables us to hire a server within minutes. Users only need to select their requirement(s) to proceed, which makes it easy and flexible to deploy applications quickly.
- Secure and Reliable
Because the data is stored in AWS data centres, AWS provides security and protects privacy. AWS infrastructure is designed to keep the data safe, and AWS manages the highest standard of security.
Examples of AWS services we are using in the field of data science are:
- Amazon S3, AWS Lambda and AWS Glue for Data Engineering such as extract, transform and load (ETL) processes.
- Amazon QuickSight for data visualization.
- Amazon Sagemaker for building, training and deploying machine learning models.
- Amazon ECS for deploying machine learning models in containers.
As was mentioned above, we find AWS to be the most mature cloud solution. Therefore, this is our first choice when it comes to bringing data science to production. However, if the circumstances require it, we are also happy to work with Microsoft Azure and Google Cloud.