---
title: "Turbo-Charging ML Development"
description: "Ramp describes Turbo, a YAML-based configuration system created to reduce the time and engineering effort required to deploy machine-learning models while preserving reproducibility and explainability. Its abstraction represents end-to-end feature and model pipelines, datasets, and shared train and predict jobs; Python entities and aggregates built with Pydantic are materialized through JobSpec and executed locally or remotely before models are stored. A developer can run a training workflow with a YAML file and a few lines of code, then adapt the same schema for prediction by changing a few configuration lines. The first Credit Risk model required nearly four months and more than 3,000 lines of deployment code; with Turbo, new models deploy in under five minutes using about 40 YAML lines on average. The system also standardizes practices such as holiday alignment in time-series models and supports nearly 20 deployed models built by nine developers."
---

# Turbo-Charging ML Development

[Ramp](https://yomu.fyi/company/ramp) · Ryan Stevens, Ryne Carbone · Feb 10, 2025

**Type:** Problem & solution

## Summary

Ramp describes Turbo, a YAML-based configuration system created to reduce the time and engineering effort required to deploy machine-learning models while preserving reproducibility and explainability. Its abstraction represents end-to-end feature and model pipelines, datasets, and shared train and predict jobs; Python entities and aggregates built with Pydantic are materialized through JobSpec and executed locally or remotely before models are stored. A developer can run a training workflow with a YAML file and a few lines of code, then adapt the same schema for prediction by changing a few configuration lines. The first Credit Risk model required nearly four months and more than 3,000 lines of deployment code; with Turbo, new models deploy in under five minutes using about 40 YAML lines on average. The system also standardizes practices such as holiday alignment in time-series models and supports nearly 20 deployed models built by nine developers.

## Context

Ramp needed to reduce the time required to put machine-learning models into production while making pipelines maintainable, reproducible, and explainable. Its first Credit Risk model took nearly four months and more than 3,000 lines of deployment code, and the company wanted retraining and model ownership to be easier for developers and on-call engineers.

## Approach / What changed

Turbo uses YAML to define end-to-end machine-learning workflows. Python entities and aggregates built with Pydantic are assembled into a JobSpec, which supports shared train and predict jobs, dataset construction, feature and model pipelines, execution in local or remote environments, and model-store persistence.

## Takeaways

- Turbo reduced new model deployment from nearly four months for the first Credit Risk model to under five minutes, with about 40 YAML lines required on average.
- Train and predict jobs share concepts and data, so a prediction configuration can be created by modifying a training YAML file rather than learning a separate schema.
- Migrating time-series models to Turbo provided consistent holiday alignment, addressing differing holiday handling that had introduced inconsistent forecasts.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Python](https://yomu.fyi/topic/python)

- Source: [Ramp](https://builders.ramp.com/post/turbo-ml-configuration-system)
- Source URL: https://builders.ramp.com/post/turbo-ml-configuration-system
- Ingested by Yomu: 2026-09-01T01:34:23.418Z

[Read original post](https://builders.ramp.com/post/turbo-ml-configuration-system)