Apache SystemDS website screenshot

Apache SystemDS

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.

Apache SystemDS publishes 1 API on the APIs.io network. Tagged areas include AutoML, Data Science, Distributed Computing, Machine Learning, and Open Source.

Apache SystemDS’s developer surface includes documentation, developer portal, getting-started guide, release notes, and 4 more developer resources.

27.8/100 emerging ▬ flat Agent 3/100 human only Full breakdown ↓
scored 2026-07-28 · rubric v0.6
AccessFreemium
1 APIs 6 Features 3 Use Cases
AutoMLData ScienceDistributed ComputingMachine LearningOpen Source

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-28 · rubric v0.6
Composite quality — 27.8/100 · emerging
Contract Quality 0.0 / 25
Developer Ergonomics 5.7 / 20
Commercial Clarity 10.0 / 20
Operational Transparency 6.2 / 13
Governance 0.0 / 12
Discoverability 5.9 / 10
Agent readiness — 3/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 10
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
A2A Agent Card 0 / 8
Dry-Run / Simulate Mode 0 / 4
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/apache-systemds: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

Apache SystemDS Python API

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-...

Pricing Plans 1

Published pricing tiers and plan structures.

Rate Limits 1

Documented rate limits and quota policies.

Apache Systemds Rate Limits

5 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Features 6

Notable capabilities this provider offers.

Declarative ML Language (DML)

High-level R-like language for specifying ML algorithms with automatic optimization.

Automatic Optimization

Query optimization, memory management, and execution plan selection for ML workloads.

Federated Learning

Privacy-preserving federated ML across distributed data silos without data sharing.

Built-In Algorithms

50+ built-in ML algorithms including linear models, neural networks, clustering, and ensemble methods.

Python API

Pythonic API for ML pipeline development with lazy evaluation and distributed execution.

Data Cleaning Pipelines

Automated data cleaning, imputation, encoding, and normalization pipelines.

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Apache Systemds Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Apache Systemds Vulnerability Disclosure

security.txt · contact published

SECURITY

Use Cases 3

What developers build with this provider.

Distributed ML Training

Train large-scale ML models distributed across Apache Spark clusters.

Federated Machine Learning

Cross-silo federated learning for privacy-sensitive healthcare and finance data.

End-to-End ML Pipelines

Integrated data preparation, feature engineering, training, and serving pipelines.

Integrations 3

Pre-built integrations with other platforms and tools.

Apache Spark

Native Spark backend for distributed matrix operations and ML training.

Python

Python API with NumPy-compatible Matrix type for local and distributed computation.

Kubernetes

Kubernetes deployment support for SystemDS runtime via Helm charts.

Resources

Get Started 2

Portal, sign-up, and the first successful call

Documentation 1

Reference material describing how the API behaves

Build 1

SDKs, sample code, and the tooling you integrate with

Access & Security 2

Authentication, authorization, and security posture

Operate 1

Status, limits, changes, and where to get help

Commercial 1

Pricing, plans, and the legal terms of use

Source (apis.yml)

apis.yml Raw ↑
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
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: Consumer
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
created: '2026-03-16'
modified: '2026-04-19'
url: https://raw.githubusercontent.com/api-evangelist/apache-systemds/refs/heads/main/apis.yml
specificationVersion: '0.19'
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
  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: 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