Apache SystemDS

aid: apache-systemds
name: Apache SystemDS
description: Apache SystemDS is an open-source ML system for the end-to-end data science lifecycle from data integration,
  cleaning, and feature engineering to model training, debugging, and deployment. It provides a declarative machine learning
  language (DML), automatic optimization for different execution backends (local, distributed Spark), and a Python API (SystemDS
  Python). SystemDS is an Apache Software Foundation top-level project designed for scalable ML workflows.
type: Index
deliveryModel:
  model: open-core
  license: Apache-2.0
  open_source: true
  commercial: true
  callable_host: false
  label: Open core · an OSS project plus a commercial hosted product
  confidence: high
  source:
  - license
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Freemium
  confidence: medium
  source:
  - plans
  generated: '2026-07-22'
  method: derived
position: Consuming
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/apache-systemds.png
tags:
- AutoML
- Data Science
- Distributed Computing
- Machine-Learning
- Open-Source
tags_raw:
- AutoML
- Data Science
- Distributed Computing
- Machine Learning
- Open Source
created: '2026-03-16'
modified: '2026-04-19'
url: https://raw.githubusercontent.com/api-evangelist/apache-systemds/refs/heads/main/apis.yml
specificationVersion: '0.23'
apis:
- aid: apache-systemds:apache-systemds-python-api
  name: Apache SystemDS Python API
  description: The SystemDS Python API (systemds) provides a Python interface for building end-to-end ML pipelines. It includes
    Matrix and Frame types for distributed data manipulation, built-in algorithms for preprocessing, feature engineering,
    classification, regression, and clustering, and federated learning capabilities. The Python API communicates with a SystemDS
    runtime backend via gRPC for distributed execution on Apache Spark.
  humanURL: https://apache.github.io/systemds/api/python/
  tags:
  - Python
  - Machine-Learning
  - Data Science
  - Distributed Computing
  tags_raw:
  - Python
  - Machine Learning
  - Data Science
  - Distributed Computing
  properties:
  - type: Documentation
    url: https://apache.github.io/systemds/api/python/
  - type: SDKs
    url: https://pypi.org/project/systemds/
    title: Python Package (PyPI)
  - type: SDKs
    url: https://search.maven.org/search?q=org.apache.systemds
    title: Java/Scala Maven Package
common:
- type: CodeOfConduct
  url: https://github.com/apache/.github/blob/main/.github/CODE_OF_CONDUCT.md
- type: ContributionGuide
  url: https://github.com/apache/systemds/blob/main/CONTRIBUTING.md
- type: License
  name: Apache-2.0
  url: https://github.com/apache/systemds/blob/main/LICENSE
- type: VulnerabilityDisclosure
  url: security/apache-systemds-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/apache-systemds-domain-security.yml
- type: GitHubRepository
  url: https://github.com/apache/systemds
- type: Documentation
  url: https://apache.github.io/systemds/
- type: Portal
  url: https://systemds.apache.org/
- type: GettingStarted
  url: https://apache.github.io/systemds/get-started
- type: ReleaseNotes
  url: https://github.com/apache/systemds/releases
- type: TermsOfService
  url: https://www.apache.org/licenses/
- type: Features
  data:
  - name: Declarative ML Language (DML)
    description: High-level R-like language for specifying ML algorithms with automatic optimization.
  - name: Automatic Optimization
    description: Query optimization, memory management, and execution plan selection for ML workloads.
  - name: Federated Learning
    description: Privacy-preserving federated ML across distributed data silos without data sharing.
  - name: Built-In Algorithms
    description: 50+ built-in ML algorithms including linear models, neural networks, clustering, and ensemble methods.
  - name: Python API
    description: Pythonic API for ML pipeline development with lazy evaluation and distributed execution.
  - name: Data Cleaning Pipelines
    description: Automated data cleaning, imputation, encoding, and normalization pipelines.
- type: UseCases
  data:
  - name: Distributed ML Training
    description: Train large-scale ML models distributed across Apache Spark clusters.
  - name: Federated Machine Learning
    description: Cross-silo federated learning for privacy-sensitive healthcare and finance data.
  - name: End-to-End ML Pipelines
    description: Integrated data preparation, feature engineering, training, and serving pipelines.
- type: Integrations
  data:
  - name: Apache Spark
    description: Native Spark backend for distributed matrix operations and ML training.
  - name: Python
    description: Python API with NumPy-compatible Matrix type for local and distributed computation.
  - name: Kubernetes
    description: Kubernetes deployment support for SystemDS runtime via Helm charts.
maintainers:
- FN: Kin Lane
  email: info@apievangelist.com