Which package is utilized to define and interact with pipelines and components in machine learning workflows?

The package that is specifically designed to define and interact with pipelines and components in machine learning workflows is the one associated with the Google Cloud ecosystem, particularly Kubeflow Pipelines. This package, often referred to as kfp.dsl, provides a domain-specific language (DSL) for the construction of complex workflows, allowing data scientists and machine learning engineers to easily create reproducible and scalable pipelines.

Using kfp.dsl, users can define the structure of their machine learning workflows by creating components that represent individual steps in the pipeline, such as data preprocessing, model training, and evaluation. This component-based approach enables seamless integration and makes it easier to manage dependencies, monitor execution, and utilize various machine learning frameworks within a unified environment.

In contrast, while mlflow is useful for tracking experiments and managing models, it does not focus specifically on workflow definition. The tf.estimator package is part of TensorFlow and primarily facilitates simplifying the training and evaluation of machine learning models but does not deal directly with defining workflows. Lastly, pandas is a powerful data manipulation library, but it does not provide the tools needed for constructing and managing machine learning pipelines. Thus, kfp.dsl is the most suitable choice for managing end-to-end