Apache Spark MLib
Apache Spark MLlib is the scalable machine learning library built on Apache Spark. It provides distributed algorithms and tools for building and deploying machine learning pipelines on large-scale data, across multiple programming languages.
Key Features
Distributed ML Algorithms: Includes classification, regression, clustering, and collaborative filtering.
Pipeline API: Tools for featurization, model tuning, and evaluation in structured ML workflows.
DataFrame-Based Workflows: Leverages Spark SQL and structured APIs for streamlined processing.
Persistence & Portability: Supports saving and loading ML models and entire pipelines.
Multi-Language Support: Compatible with Java, Scala, Python (PySpark), and R.
Example Use Cases
Building scalable ML pipelines for production systems
Training models on distributed data in Spark environments
Performing large-scale statistical analysis and feature engineering
Integrating ML into ETL workflows or real-time streaming apps