OrchestraML
Turn natural-language ML goals into auditable ML pipelines.
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OrchestraML - Turn natural-language ML goals into auditable ML pipelines.

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OrchestraML is a multi-agent AI platform that converts a natural-language ML objective and CSV dataset into a complete ML workflow. It guides users through dataset guardrails, EDA, cleaning, feature engineering, model training, evaluation, AI audit, PDF reporting, and downloadable model bundles - with Human-in-the-Loop checkpoints at key decisions. It is built for students, developers, and early-stage teams who want to build ML models faster without losing trust, explainability, or control.

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Hey Product Hunt 👋 I’m Sameer, a Computer Engineering student and the maker of OrchestraML. I built OrchestraML because creating a machine-learning pipeline is still too fragmented for many students and developers. You need to understand EDA, cleaning, feature engineering, model selection, evaluation, explainability, and deployment packaging before you can even get a usable model. OrchestraML turns a simple ML goal and CSV dataset into a guided multi-agent workflow. In this new version, I focused on making the platform more trustworthy and production-ready: Multi-agent pipeline execution Human-in-the-Loop checkpoints Dataset guardrails and leakage detection Automated EDA, cleaning, and feature engineering Model training and evaluation Model trust score and AI audit trail Professional PDF report generation Downloadable model bundle Fully upgraded responsive UI The goal is not to replace data scientists. The goal is to help students, developers, and early teams build ML workflows faster while still understanding what the system is doing. I’d love your feedback, especially on: How clear the pipeline experience feels Whether the checkpoints make ML easier to understand What datasets you’d like OrchestraML to support better Thanks for checking it out !!