Amazon SWF vs Azure Machine Learning

Amazon SWF vs Azure Machine Learning: What are the differences?

What is Amazon SWF? Automate the coordination, auditing, and scaling of applications across multiple machines. Amazon Simple Workflow allows you to structure the various processing steps in an application that runs across one or more machines as a set of “tasks.” Amazon SWF manages dependencies between the tasks, schedules the tasks for execution, and runs any logic that needs to be executed in parallel. The service also stores the tasks, reliably dispatches them to application components, tracks their progress, and keeps their latest state.

What is Azure Machine Learning? A fully-managed cloud service for predictive analytics. Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.

Amazon SWF belongs to "Cloud Task Management" category of the tech stack, while Azure Machine Learning can be primarily classified under "Machine Learning as a Service".

Some of the features offered by Amazon SWF are:

• Maintaining application state
• Tracking workflow executions and logging their progress
• Holding and dispatching tasks

On the other hand, Azure Machine Learning provides the following key features:

• Designed for new and experienced users
• Proven algorithms from MS Research, Xbox and Bing
• First class support for the open source language R

According to the StackShare community, Azure Machine Learning has a broader approval, being mentioned in 12 company stacks & 8 developers stacks; compared to Amazon SWF, which is listed in 11 company stacks and 3 developer stacks.