Python pyspark.ml.Pipeline is a machine learning library in PySpark that provides a high-level API for constructing, training, and executing machine learning pipelines. It allows users to define a sequence of stages, where each stage represents a specific data transformation or model training step. This pipeline-based approach enables efficient and reproducible machine learning workflows by automating the data preparation, feature extraction, model training, and evaluation processes. With the help of pyspark.ml.Pipeline, users can easily build complex ML workflows and apply them to large-scale datasets in a distributed and parallel manner using Spark's distributed computing capabilities.
Python Pipeline - 60 examples found. These are the top rated real world Python examples of pyspark.ml.Pipeline extracted from open source projects. You can rate examples to help us improve the quality of examples.