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.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Apache SystemDS the way a machine reads it — 18 machine-readable artifacts across 1 API, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Apache SystemDS scores 29.8/100 (emerging), with a separate agent-readiness read of 7/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Apache SystemDS
Each block below is one kind of artifact we hold for Apache SystemDS. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 1
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
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
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Apache Systemds Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Apache Systemds Finops
FINOPSFeatures 6
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
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 — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Use Cases 3
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
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 tell you where this provider already fits in a stack.
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
Every other property we hold for Apache SystemDS — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
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
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