In an ML workflow, what does a pipeline represent?

In a machine learning (ML) workflow, a pipeline represents a sequence of data processing steps. Pipelines are essential as they define the flow of data through various stages, from initial data collection and preprocessing to model training and evaluation. Each step within the pipeline can include tasks such as data cleaning, feature extraction, model fitting, and prediction. By structuring these processes into a pipeline, each stage's inputs and outputs are clearly defined, which enhances reproducibility, improves organization, and allows for easier adjustments and optimizations.

In contrast to other options, an isolated model refers to a specific trained model that is not necessarily part of a broader workflow, while a container for machine learning resources could signify infrastructure aspects rather than the logical sequence of operations. An ML workflow description might outline the entire process but does not encapsulate the detailed step-by-step nature provided by a pipeline. Thus, the option that accurately defines a pipeline is the sequence of data processing steps, distinguishing it from these other concepts.