08. Machine Learning Applications

Notes:

All algorithms used within the machine learning workflow are similar for both the cloud and on-premise computing. The only real difference may be in the user interface and libraries that will be used to execute the machine learning workflow.

For personal use , one’s likely to use cloud services , if they don’t have enough computing capacity.

With academic use , quite often one will use the university’s on-premise computing resources, given their availability. For smaller universities or research groups with few funding resources, cloud services might offer a viable alternative to university computing resources.

For workplace usage , the amount of cloud resources used depends upon an organization’s existing infrastructure and their vulnerability to the risks of cloud computing. A workplace may have security concerns, operational governance concerns, and/or compliance and legal concerns regarding cloud usage . Additionally, a workplace may already have on-premise infrastructure that supports the workflow; therefore, making cloud usage an unnecessary expenditure. Keep in mind, many progressive companies may be incorporating cloud computing into their business due to the business drivers and benefits of cloud computing.