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Building a machine learning model is still a long chain of specialized steps — finding data, cleaning it, engineering features, training, and shipping. OrchestraML compresses that whole lifecycle into a single described goal. You tell it what you want in plain English, and its agents handle the rest, with a human signing off at every critical decision.

## How OrchestraML works

Describe the ML goal and the agents run the pipeline: dataset search, exploratory data analysis, cleaning, feature engineering, AutoML model selection, and deployment. The deliberate design choice is the human-in-the-loop checkpoint — nothing critical advances without approval, so you stay in control of which data is used and which model ships, rather than handing the whole thing to a black box.

## Why it matters

Most “AutoML” tools automate the modeling step but leave the messy data wrangling and deployment plumbing to you. By treating the entire lifecycle as an agentic workflow — with approval gates instead of full autonomy — OrchestraML aims at the part of ML that actually eats the time, while keeping the accountability a real model pipeline needs.