There is rarely "one pipeline" to manage an E2E process. And there is rarely "one article" to understand a new concept. Understanding MLOps & deciphering jargon such as CI/CD/CT, automation, & deployment rely heavily on the context of our workflow architecture. Deploying a machine learning model into production can involve multiple pipelines that contribute to one large data science workflow. For instance, a Data Engineer prepares the data by sourcing it from a data lake and this is absorbed into the data catalog.

Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible.