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Customer Story · Ramp

Ramp’s ML platform, rebuilt for the agent era

Ramp’s ML platform team rebuilt their orchestration layer on Prefect in a single quarter. They migrated 200 flows off Metaflow, grew to 70+ active contributors, and gave new users a 30-minute path from idea to production.

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Ramp×Prefect
Watch the 2-minute testimonial↓

Ramp on Prefect

Ramp moves fast. We need partners who do the same. We’re making a bet on not just the tool as it is today, but also where it’s going.
Ryne CarboneRyne CarboneStaff Machine Learning Engineer, Ramp

Watch the story

Rebuilding Ramp’s ML Platform

Two minutes on why the team chose Prefect and what changed.

200Metaflow flows migrated in one quarter
350Deployed Prefect flows today
70+Active contributors company-wide
30mFrom idea to production
Prefect is very open-ended, which is great. Being opinionated matters, and it should be the company or your ML platform team doing that. Not the framework.
Ryne CarboneRyne CarboneStaff Machine Learning Engineer, Ramp

From Metaflow to Prefect

A one-quarter migration off Metaflow

When Metaflow’s opinionated model started getting in the way, Ramp’s ML platform team rebuilt their orchestration layer on Prefect. Three months later, they had ported every flow, and their users had already added more on top.

Before

200

Metaflow flows

Migrated

After

350

Prefect flows, one quarter later

How Ramp uses Prefect

A short path from idea to production

One platform for technical and non-technical contributors, and agents that can debug their own flows.

Python decorators

Add @flow and ship

Ramp's developers add @flow or @task to Python they're already writing and the code is ready to deploy. Going from prototype to production didn't require much refactoring, and Prefect stayed out of the way of testing and running workflows.

Templating and event triggers

One flow, many configurations

Ramp's daily ML batch predict is a single Python flow with Prefect decorators. Users configure their own input parameters, event triggers, schedules, and resources, then deploy variants separately while the platform team maintains the core.

CLI, API, and skills

Coding agents debug Prefect directly

Ramp connects its coding agents to the Prefect CLI, API, and skills to investigate infrastructure and flow failures. Their Automated Debugger watches the alerts channel, investigates failures through an internal agent harness, and drafts a PR for review.

On developer experience

Prefect felt mostly like writing Python code. Going from prototype to production didn’t really involve much refactoring.
Ryne CarboneRyne CarboneStaff Machine Learning Engineer, Ramp

Infrastructure decorators

There’s two fronts where infrastructure decorators help, and they’re both during development. They bridge the gap between development and production, and they unlock extra compute, bigger machines than your laptop has.
Ryne CarboneRyne Carbone · Staff Machine Learning Engineer, Ramp
daily_predict.py
1from prefect import flow, task
2from prefect_aws.ecs import ecs
3
4
5@task(retries=2)
6def load_features(date: str) -> list[dict]:
7 return warehouse.fetch(date)
8
9
10@task
11def predict(features: list[dict]) -> list[float]:
12 return model.score(features)
13
14
15# One decorator: production-like permissions + bigger compute.
16@ecs(cpu=4096, memory=16384)
17@flow(name="daily-predict")
18def daily_predict(date: str):
19 features = load_features(date)
20 return predict(features)
Read the deep dive on infrastructure decorators

Six months in

In our ML platform repo, we’ve had more commits in the past six months than our entire history before that.
Ryne CarboneRyne CarboneStaff Machine Learning Engineer, Ramp
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Try Prefect for your team

Prefect gives platform teams the flexibility to set their own patterns and gives every contributor a short path from idea to production.

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