Getting Started

CorePlexML exposes a comprehensive REST API for automating every stage of the ML lifecycle: data ingestion, model training, deployment, monitoring, privacy compliance, and synthetic data generation.

There are two ways to interact with the API:

  1. Python SDK – A typed, resource-oriented client library. Install with pip install coreplexml and get started in minutes.

  2. REST API – Direct HTTP calls using curl, requests, or any HTTP library. Every endpoint is documented with request/response examples in the API Reference section.

Core Concepts

Projects

Projects are the top-level organizational unit. Every dataset, experiment, model, and deployment belongs to exactly one project. Think of a project as a workspace for a single ML use case (e.g., “Customer Churn Prediction”).

Datasets & Versions

Upload CSV files to a project. CorePlexML automatically profiles the data, detects column types, and creates an immutable dataset version. Each subsequent upload creates a new version, preserving full data lineage.

Experiments

An experiment runs H2O AutoML against a dataset version and target column. It automatically trains, tunes, and ranks dozens of candidate models (GBM, XGBoost, Deep Learning, Stacked Ensembles, etc.). Experiments are asynchronous background jobs – you launch them and poll for completion.

Models

Each experiment produces multiple ranked models. Inspect model metrics (AUC, RMSE, MAE, etc.), feature importance, hyperparameters, and make ad-hoc predictions.

Deployments (MLOps)

Deploy any model to a REST prediction endpoint. Deployments support staging/production stages, canary rollouts, alerting, and automatic retraining triggers.

Reports

Generate PDF reports for experiments, models, and projects with charts, metrics tables, and optional AI-powered recommendations.

Enterprise Modules

  • Privacy Suite – PII detection across 72+ types with automated masking, hashing, and redaction. HIPAA, GDPR, PCI-DSS, and CCPA compliance profiles.

  • SynthGen – Train CTGAN, CopulaGAN, or TVAE models to produce privacy-safe synthetic datasets that preserve statistical properties.

  • What-If Studio – Scenario-based model exploration. Change feature values and compare prediction outcomes side-by-side.

Next Steps