Microsoft ML for Apache Spark
An open-source library for building massively scalable machine learning pipelines on Apache Spark.
What is Microsoft ML for Apache Spark?
SynapseML (formerly MMLSpark) is an open-source library that simplifies the creation of massively scalable machine learning pipelines. Built on Apache Spark, it provides simple, composable, and distributed APIs for a wide variety of ML tasks such as text analytics, vision, anomaly detection, and deep learning, enabling seamless integration into existing Spark workflows. It solves the problem of unifying machine learning ecosystems at massive scales by abstracting over diverse data sources and computational environments.
Data engineers and data scientists working with large-scale datasets who need to build and deploy scalable ML pipelines within Apache Spark ecosystems, such as on Azure Synapse Analytics, Databricks, or HDInsight. It is also suitable for teams requiring distributed inference, integration with Microsoft Cognitive Services at scale, or responsible AI tools in Spark.
Developers choose SynapseML for its seamless integration with SparkML APIs, allowing them to extend existing Spark workflows with advanced ML capabilities without a steep learning curve. Its unique selling point is providing a unified library that combines distributed algorithms (like LightGBM, Vowpal Wabbit), cognitive services at scale, ONNX inference, and responsible AI tools, all while supporting multiple programming languages and elastic cluster scaling.
Overview
Simple and Distributed Machine Learning
Use Cases
Best For
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