Infrastructure and resource management
The infrastructure and resources needed for the machine learning software are organized into two areas – data serving infrastructure (for example, databases) and computational infrastructure (for example, GPU computing platforms). There is also serving infrastructure, which is used to provide the services to the end users. The serving infrastructure could be in the form of desktop applications, embedded software (such as the one in autonomous vehicles), add-ins to tools (as in the case of GitHub Co-pilot), or websites (such as ChatGPT). However, in this book, we’ll focus on the data-serving infrastructure and the computational infrastructure.
Both areas can be deployed locally or remotely. Local deployment means that we use our own infrastructure at the company, while remote infrastructure means that we use cloud services or services of another supplier.
Conceptually, we could see these two areas as co-dependent, as depicted...
