Apache MADlib
Apache MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical, and machine learning methods for structured and unstructured data, executed within PostgreSQL or Greenplum Database. MADlib enables data scientists to run machine learning algorithms directly in the database using SQL.
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 MADlib the way a machine reads it — 25 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 MADlib scores 24.5/100 (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 MADlib
Each block below is one kind of artifact we hold for Apache MADlib. 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 MADlib
MADlib provides SQL-callable functions for classification, regression, clustering, dimensionality reduction, graph analytics, time series analysis, deep learning with Keras/Tens...
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 Madlib 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 Madlib Finops
FINOPSFeatures 9
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.
In-Database Machine Learning
Run machine learning algorithms directly within PostgreSQL or Greenplum Database using SQL, eliminating data movement overhead.
Classification and Regression
Support for logistic regression, linear regression, naive Bayes, decision trees, random forests, support vector machines, and more.
Clustering Algorithms
K-Means, DBSCAN, and other clustering algorithms for unsupervised learning within the database.
Deep Learning with Keras/TensorFlow
Train and serve deep learning models using Keras and TensorFlow backends with GPU acceleration support.
Graph Analytics
Built-in graph algorithms for network analysis, path finding, and community detection on graph data stored in the database.
Time Series Analysis
ARIMA, SARIMA, and other time series forecasting models running in-database.
Dimensionality Reduction
PCA and SVD implementations for dimensionality reduction and feature extraction.
Model Selection and Hyperparameter Tuning
Cross-validation and hyperparameter optimization frameworks for model selection.
Association Rules
FP-Growth and Apriori algorithms for market basket analysis and association rule mining.
Scroll within the panel for all 9 ·
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 5
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.
Predictive Analytics
Build predictive models for churn prediction, fraud detection, and demand forecasting directly on database data.
Recommendation Systems
Implement collaborative filtering and content-based recommendation algorithms using in-database machine learning.
Customer Segmentation
Cluster customers using K-Means and other algorithms to identify segments for targeted marketing.
Anomaly Detection
Detect anomalies in time series and transactional data using statistical models running in-database.
Network Analysis
Analyze social networks, supply chains, and communication graphs using built-in graph algorithms.
Integrations 5
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
PostgreSQL
Primary execution environment supporting PostgreSQL versions 11 through 15.
Greenplum Database
Native support for Greenplum Database GP6 and GP7 for massively parallel processing.
TensorFlow
Deep learning backend integration for training neural networks within the database.
Keras
High-level deep learning API integration for building and training models with GPU acceleration.
XGBoost
Gradient boosting framework integration for high-performance tree-based models.
Resources
Every other property we hold for Apache MADlib — 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 1
Portal, sign-up, and the first successful call
Build 2
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
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/apache-madlib · machine-readable index on apis.io