About OrchestraML
Overview
OrchestraML is a multi-agent machine learning platform that turns a stated objective and a dataset into a validated, explainable, exportable pipeline. Nine specialized agents handle planning, data sourcing, risk detection, profiling, cleaning, feature engineering, AutoML training, evaluation, and output generation, while you approve each critical decision.
The platform targets the gaps of black-box AutoML, where preprocessing choices stay hidden and outputs are hard to reproduce. OrchestraML instead shows each agent's reasoning, blocks modeling on flagged dataset risks, scores deployment readiness, and records every recommendation and approval in an audit log.
Key Benefits
- Nine agents cover the whole ML lifecycle in a single orchestrated run, from objective parsing to output generation.
- Human-in-the-loop checkpoints pause the pipeline so you approve, reject, or modify agent decisions before they take effect.
- Guardrails intercept target leakage, identifier and PII columns, and near-constant features before any model trains.
- The feature agent accepts only candidates that prove cross-validation gain and stability against a cleaned baseline.
- Evaluation translates accuracy into a trust score, generalization gap, bias check, SHAP importance, and deployment readiness.
- Every run ends with a reproducible zip bundle holding the model, preprocessor, inference script, dependencies, and PDF report.
How It Works
You describe the ML goal in plain English, and the Orchestrator plans the pipeline while identifying the target, task type, risks, and success metric. You upload a CSV or let the Dataset agent source one from Hugging Face or Kaggle, then review the plan, dataset risks, target columns, and feature strategy at each pause point. At the end you pick the champion model, review the tabbed report and AI audit trail, and download the zip bundle.
Use Cases
- Data scientists at small teams run churn, fraud, or scoring models without hand-building preprocessing and validation pipelines.
- Students and learners use the free plan to run up to 10 pipelines a month on datasets up to 50,000 rows with auto-sourced data.
- Analytics teams in regulated settings keep an audit trail of agent recommendations and human approvals for internal review.
- ML engineers hand off the exported zip with a ready inference script and pinned dependencies to a deployment environment.
- Team leads share a pipeline library and model registry across five team members on the Team plan.
- Consultants use trust-score breakdowns and SHAP importance to explain model behavior to non-technical stakeholders.
Why Choose This Product
OrchestraML differentiates itself through explicit agent decisions rather than silent automation: dataset risk validation runs before modeling, humans approve critical stages, and every step lands in an audit log. It also ships a complete export bundle, so a finished run leaves you with a deployable package instead of metrics alone. The platform states plainly that its Trust Score is an analytical tool, not a guarantee of real-world safety.
OrchestraML Pros & Cons
- Nine specialized agents cover the full ML lifecycle in one run
- Human approval gates at six checkpoints on the free plan
- Guardrails flag leakage and PII columns before training
- Export bundle ships model, preprocessor, inference script and PDF report
- Free plan is free forever with no credit card required
- Premium plans are still being finalized and the payment system is not live yet
- Free plan limits users to 10 pipelines per month and 50,000-row datasets
- Trust Score is an analytical tool, not a guarantee of real-world safety
Key Features
Multi-Agent Orchestration
Nine specialized agents cover pipeline planning, data sourcing, guardrails, EDA, cleaning, feature engineering, AutoML training, evaluation, and output generation in one coordinated run.
Human Review Checkpoints
The pipeline pauses at critical stages so you can approve, reject, or modify the agents' risk assessment, feature strategy, and model selection before work continues.
Dataset Guardrails
A dedicated guardrails agent scans for target leakage, identifier and PII columns, duplicate columns and target representations, constant features, suspicious correlations, and high-cardinality categoricals.
Validated Feature Engineering
The feature agent builds a cleaned baseline, engineers candidates, and only passes features that show proven cross-validation gain and stability.
Trust-Aware Evaluation
Evaluation reports task-aware metrics, a model trust score, generalization gap, bias check, SHAP feature importance, and business value translation.
Reproducible Export Bundle
Each run produces a zip with model.pkl, preprocessor.pkl, predict.py, requirements.txt, sample_input.json, and a PDF report.
Pipeline Audit Log
Every agent recommendation and user approval is recorded from the first dataset scan through final model selection.
Dataset Sourcing
You upload a CSV or let the Dataset agent find a suitable dataset automatically from Hugging Face or Kaggle.
AutoML Model Search
All plansThe Modeling agent trains and compares candidate models inside a time budget and logs winning hyperparameters and validation scores.
Tabbed Pipeline Report
Completed runs open a report with overview, EDA, cleaning, features, model, evaluation, AI audit, and deployment tabs plus a downloadable PDF.
OrchestraML Pricing
View full pricing →- 10 pipelines per month
- Datasets up to 50,000 rows
- All 8 pipeline stages
- 6 human checkpoints
- Model download (ZIP)
- EDA charts + SHAP and AI audit trail
- Unlimited pipelines
- Datasets up to 500,000 rows
- BentoML API deployment
- Jupyter notebook export
- Priority processing
- Priority email support
- 5 team members
- Shared pipeline library
- Team model registry
- Slack notifications
- Dedicated support
- Custom data retention
| Feature | Free Free/mo | Pro ₹1249/mo | Team ₹3199/mo |
|---|---|---|---|
| 10 pipelines per month | |||
| Datasets up to 50,000 rows | |||
| All 8 pipeline stages | |||
| 6 human checkpoints | |||
| Model download (ZIP) | |||
| EDA charts + SHAP and AI audit trail | |||
| Unlimited pipelines | |||
| Datasets up to 500,000 rows | |||
| BentoML API deployment | |||
| Jupyter notebook export | |||
| Priority processing | |||
| Priority email support | |||
| 5 team members | |||
| Shared pipeline library | |||
| Team model registry | |||
| Slack notifications | |||
| Dedicated support | |||
| Custom data retention |
Pricing extracted from the product website and may change. Check the source for current details.
Frequently asked questions about OrchestraML
Is the OrchestraML free plan really free forever?
Yes. The Free plan costs ₹0 per month forever, includes 10 pipelines per month, datasets up to 50,000 rows, all 8 pipeline stages, 6 human checkpoints, model download as a ZIP, EDA charts with SHAP, and an AI audit trail, and requires no credit card to sign up.
What do I get at the end of a pipeline run?
OrchestraML returns a reproducible zip bundle containing model.pkl, preprocessor.pkl, predict.py for inference, requirements.txt with exact dependencies, sample_input.json as a test payload, and report.pdf with the exported pipeline report.
Can I control what the agents decide?
Yes. The pipeline pauses at critical stages and shows what the agents found, their reasoning, and their recommended action. You then approve, reject, or modify decisions such as the pipeline plan, target and columns, dataset risk drops, feature strategy, and final model.
Does OrchestraML check my dataset for problems?
The Guardrails agent scans datasets for target leakage, duplicate target representations, identifier and PII columns, constant and near-constant features, duplicate columns, suspicious correlations, and high-cardinality categoricals, then recommends specific drops before EDA and training.
Can I upload my own dataset or have OrchestraML find one?
You can upload a CSV yourself, or let the Dataset agent automatically search for a suitable dataset from Hugging Face or Kaggle. Free tier datasets are capped at 50,000 rows, while Pro raises the limit to 500,000 rows.
How does OrchestraML evaluate a trained model?
Evaluation covers task-aware metrics, generalization analysis, a model trust score with breakdown, residual or balanced metrics, SHAP feature importance, business value translation, and deployment readiness, so accuracy is not the only signal.