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.
Kin Score
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
RATE LIMITSFinOps 1
Cost, billing, and metering signals for API financial operations.
Apache Systemds Finops
FINOPSFeatures 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.
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