Configuration and monitoring
Machine learning software is meant to be professionally engineered, deployed, and maintained. Modern companies call this process MLOps, which means that the same team needs to take responsibility for both the development and operations of the machine learning system. The rationale behind this extended responsibility is that the team knows the system best and therefore can configure, monitor, and maintain it in the best possible way. The teams know the design decisions that must be taken when developing the system, assumptions made about the data, and potential risks to monitor after the deployment.
Configuration
Configuration is one such design decision that’s made by the development team. The team configures the parameters of the machine learning models, the execution environment, and the monitoring infrastructure. Let’s explore the first one; the latter two will be discussed in the next few sections.
To exemplify this challenge...
