Dask is a flexible library for parallel computing in Python that can be used to speed up data engineering and machine learning tasks. Dagster, a platform for building data pipelines, can be used with Dask to automate computations and make pipelines faster. A Dask resource can be defined using Dagster's resources feature to set up a Dask cluster, allowing for centralized configuration of the cluster across multiple assets. This enables simplification of pipelines by reducing redundant code in assets. The Dask UI can be accessed to monitor executions from Dagster, which are only available while the executions are in progress. Using Dask with Dagster allows for speeding up processes and building machine learning pipelines that utilize Dask's parallel computing capabilities. A machine learning pipeline using Dask involves generating synthetic data, splitting it into training and test sets, searching for the best classification model, scoring the model, and loading the assets into a code location or Definitions. The use of Dagster with Dask bridges business intelligence and data orchestration, allowing for the orchestration of unstructured data pipelines.