CorePlexML
AutoML, MLOps, Privacy Suite, Synthetic Data, ML Studio & AI-Powered Dataset Builder — one integrated platform that replaces 5 different tools.
Getting a model from experiment to production requires stitching together 5-8 separate tools — each with its own learning curve, pricing, and failure modes.
Jupyter + MLflow + Airflow + Seldon + Great Expectations + custom scripts. Each team builds its own glue code.
PII detection, data masking, and audit trails bolted on as afterthoughts. HIPAA/GDPR compliance becomes a manual process.
Months from experiment to deployment. Models rot in notebooks while data scientists wait for engineering resources.
The result: 87% of ML models never reach production. The gap isn't talent — it's tooling.
Your ML team is wasting 60% of its time on operational overhead. One platform replaces the 6-7 disconnected tools slowing your team down.
Engineering time redirected from infra to model building every month
Time to production from raw data to live endpoint
Tools consolidated — more experiments, fewer context switches
CorePlexML replaces your entire ML stack with six integrated modules — from data preparation to production monitoring.
Multi-Engine AutoML (H2O + FLAML)
Registry, canary, A/B, drift, auto-retrain
72+ PII types, HIPAA/GDPR/PCI-DSS/CCPA
CTGAN, CopulaGAN, TVAE synthetic data
What-If analysis, SHAP, PDP, fairness
AI-powered data prep, no code needed
From raw data to deployed models with monitoring — in hours, not months.
CSV, Excel, JSON, XML — drag & drop your dataset and let the AI assistant prepare it. Automatic missing value handling, outlier detection, and type inference.
AutoML selects the best algorithm, tunes hyperparameters, and builds ensemble models automatically. Multi-Engine AutoML (H2O + FLAML), including GBM, XGBoost, Deep Learning, and Stacked Ensembles.
One-click deployment with canary rollouts, drift detection, and auto-retraining. Real-time inference API with WebSocket streaming. A/B testing with automatic winner declaration.
Run H2O and FLAML in parallel on the same dataset. Each engine explores different optimization strategies independently. CorePlexML picks the best model across all engines.
XGBoost, GBM, Deep Learning, Random Forest, GLM, and Stacked Ensembles with Bayesian hyperparameter tuning.
Microsoft FLAML finds optimal models within a time budget. Random Forest, Extra Trees, and Logistic Regression with scikit-learn.
Coming soon: AutoGluon Tabular, MLJAR-supervised, TPOT
| Plan | Engines | Parallel |
|---|---|---|
| Free | H2O only | — |
| Pro | H2O or FLAML | — |
| Team | H2O or FLAML | — |
| Enterprise | H2O + FLAML | ✓ Up to 3 |
Model coverage with parallel multi-engine execution
AutoML selects the best algorithms, tunes hyperparameters, and builds ensemble models. Then explore every metric, chart, and explainability view in detail.
From model registry to production deployment with canary rollouts, real-time monitoring, and automated retraining — all in one view.
Detect, mask, redact, or encrypt sensitive data across your entire pipeline. Policy-based processing with complete audit trails.
Protected health information: names, MRNs, dates of birth, diagnoses
Personal data: emails, phone numbers, addresses, national IDs
Payment data: credit card numbers, CVVs, expiration dates
California consumer data: SSNs, driver's licenses, biometric data
72+ PII types detected automatically using NLP and pattern matching. No manual tagging required.
Mask, redact, hash, encrypt, or replace with synthetic data. Configure per-column policies with rule priorities.
Every transformation logged with timestamp, user, policy, and before/after snapshots. Export-ready for compliance audits.
Create statistically faithful synthetic datasets that preserve distributions without exposing real data. Perfect for development, testing, and data augmentation.
Distribution fidelity across 42 columns
Membership inference resistance
Interactive workspace for model exploration. Create scenarios, compare against baselines, and understand model behavior — no code required.
Compare, Classify, Explain, Analyze, Advanced, Operations
Test approval thresholds
Simulate risk scenarios
Test retention levers
Analyze decision bounds
Conversational AI interface powered by LLMs. Describe your goals in natural language and the AI executes the transformations — full script transparency.
Owner, editor, and viewer roles per project. SSO with SAML 2.0, OIDC, Google and GitHub OAuth.
Automatic GPU allocation for training and inference. CUDA acceleration with intelligent CPU fallback.
Split traffic between model variants. Bayesian and Frequentist analysis with automatic winner declaration.
Semantic versioning with stage transitions. Model cards, lineage tracking, and tag-based organization.
Every action logged with user, timestamp, and context. Security events, access logs, and compliance trails.
Project-level isolation with PostgreSQL Row-Level Security. Per-tenant billing with usage metering.
Enterprise-grade ML solutions tailored for regulated and data-intensive industries.
Fraud detection, credit scoring, AML monitoring
Patient outcomes, clinical analytics, HIPAA-compliant pipelines
Demand forecasting, personalization, churn prevention
Citizen data analytics with full anonymization and compliance
Purpose-built workflows for every role in your ML organization.
| Role | Pain Point | How CorePlexML Helps | Result |
|---|---|---|---|
| Developer API-first ML integration |
ML integration complexity — different APIs for each tool | REST API & Python SDK — one unified interface for everything | Programmatic MLOps with CI/CD-ready SDK and 320+ endpoints |
| ML Engineer Full lifecycle MLOps |
Experiment sprawl and deployment risk across environments | Model registry, canary/blue-green deploys, auto-retraining | Safe rollouts with drift detection and automated pipelines |
| Data Scientist AutoML & explainability |
Algorithm selection fatigue and explainability gaps | 50+ algorithm AutoML with built-in SHAP, PDP, and fairness | More experiments, less infra — focus on model quality |
| Business Analyst No-code ML tools |
Technical barrier to scenario testing and reporting | Visual What-If Studio and automated PDF reports | Test business scenarios and generate reports without code |
| Compliance Officer Privacy & audit trails |
Manual compliance audits across multiple regulations | 72+ PII detection, HIPAA/GDPR/PCI-DSS/CCPA profiles | Full audit trails, one-click compliance reports |
One platform. All capabilities. A fraction of the cost.
| Capability | CorePlexML | DataRobot | H2O.ai | Alteryx |
|---|---|---|---|---|
| AutoML | ✓ | ✓ | ✓ | ✓ |
| MLOps & Model Registry | ✓ | ✓ | — | Partial |
| Privacy Suite (72+ PII) | ✓ | — | — | — |
| Synthetic Data Generation | ✓ | — | — | — |
| What-If Analysis | ✓ | ✓ | — | ✓ |
| A/B Testing | ✓ | Partial | — | — |
| Conversational Builder | ✓ | — | — | — |
| Open Source | ✓ | — | ✓ | — |
| Starting Price | Free | $$$$$ | $$$$ | $$$$ |
No credit card required. Upgrade when you need more. Cancel anytime.
Annual billing saves ~17%. Pro: $490/yr · Team: $1,990/yr
Start with zero risk. Scale when you're ready.
Sign up in 30 seconds. Upload data, train your first model, deploy to staging. No credit card, no time limit.
Unlock everything: unlimited models, Privacy Suite, SynthGen, canary deployments. Full access, no charge until trial ends.
Choose individual Pro ($49/mo) or Team ($199/mo for 5 seats). Annual billing saves 17%. Upgrade or downgrade anytime.
Unlimited seats, SSO/SAML, on-premise deployment, custom SLA, dedicated account manager. Talk to sales for custom pricing.
From months to hours. One-click deployment with built-in monitoring eliminates the MLOps bottleneck.
One platform instead of Jupyter + MLflow + Seldon + Great Expectations + custom privacy tools.
Generous free tier with real capabilities. No credit card. No time limit. Start shipping models today.
Built-in compliance (HIPAA, GDPR, PCI-DSS, CCPA)
REST API endpoints — fully programmable platform
CorePlexML
Join the data teams shipping models faster with CorePlexML. Start free, no credit card required.