SynapseML

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SynapseML is an open-source library that simplifies the creation of massively scalable machine learning pipelines. It provides composable and distributed APIs for various ML tasks, built on Apache Spark.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is SynapseML?

SynapseML (previously known as MMLSpark) is an open-source library designed to simplify the creation of massively scalable machine learning (ML) pipelines. It offers simple, composable, and distributed APIs for a wide variety of ML tasks, including text analytics, computer vision, anomaly detection, and deep learning. Built on the Apache Spark distributed computing framework, SynapseML shares the same API as the SparkML/MLLib library, allowing seamless integration into existing Apache Spark workflows. It supports training and evaluating models on single-node, multi-node, and elastically resizable clusters, and is usable across Python, R, Scala, Java, and .NET. Its API abstracts over various databases, file systems, and cloud data stores, simplifying experiments regardless of data location.

Best used for

Ideal for developers who need to build massively scalable machine learning pipelines, integrate advanced AI services into Apache Spark workflows, and deploy distributed models. Especially valuable for those working with large datasets and requiring cross-language support (Python, R, Scala, Java, .NET).

Common actions

build machine learning pipelines

scale AI models

integrate AI services

perform distributed computing

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Capabilities

Key features

Scalable ML pipelines
Distributed APIs
Text analytics
Computer vision
Anomaly detection
Deep learning
Apache Spark integration

Target Audience

developer

Integrations

apache-sparkmicrosoft-fabricazure-synapsedatabricks

Pricing & Plans

Open Source

Free

FAQs

What programming languages does SynapseML support?

SynapseML is designed to be usable across multiple programming languages, including Python, R, Scala, Java, and .NET. This broad support allows developers to integrate it into their preferred development environments and existing codebases.

What are the core components or features offered by SynapseML?

SynapseML offers features like Vowpal Wabbit on Spark, Cognitive Services for Big Data, LightGBM on Spark, Spark Serving, ONNX on Spark, and Responsible AI. These components enable various machine learning tasks from distributed training to model inference.

What are the system requirements for using SynapseML?

SynapseML requires Scala 2.12, Spark 3.4+, and Python 3.8+. It can be installed and configured in various environments such as Microsoft Fabric, Synapse Analytics, Databricks, and standalone Spark clusters.

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Also listed in

This tool also appears in

AI Agents & Automation › AI Frameworks & Infra Data & Analytics › Data Pipelines & Integration Research & Education › Scientific Computing

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