Commit 564a01f

title: Streamlining ML Feature Engineering with Feast and dbt

description: Learn how to leverage your dbt transformations as Feast features to eliminate duplicate work and accelerate ML development.

If you're a dbt user, you know the power of well-crafted data models. You've invested time building clean, tested, and documented transformations that your team relies on. Your dbt models represent the single source of truth for analytics, reporting, and increasingly—machine learning features.

But here's the challenge: when your ML team wants to use these models for production predictions, they often need to rebuild the same transformations in their feature store. Your beautiful dbt models, with all their logic and documentation, end up getting reimplemented elsewhere. This feels like wasted effort, and it is.

What if you could take your existing dbt models and put them directly into production for ML without rewriting anything? That's exactly what Feast's dbt integration enables.

These models are perfect for machine learning features. The aggregations you've built for your daily reports? Those are features. The customer attributes you've enriched? Features. The time-based calculations you've perfected? You guessed it—features.

The problem isn't your models—they're great. The problem is getting them into a system that can serve them for real-time ML predictions with low latency and point-in-time correctness.

Feast's dbt integration is designed with one principle in mind: **your dbt models should be the single source of truth**. Instead of asking you to rewrite your transformations, Feast reads your dbt project and automatically generates everything needed to serve those models for ML predictions.

The best part? **You don't change your dbt workflow at all.** Keep building models the way you always have. The integration simply creates a bridge from your dbt project to production ML serving.

Let's walk through a real example. Imagine you're a data engineer at a ride-sharing company, and you've already built dbt models to track driver performance. Your analytics team loves these models, and now your ML team wants to use them to predict which drivers are likely to accept rides.

Perfect use case. Let's take your existing dbt models to production ML in just a few steps.

In seconds, Feast generates a complete Python file with everything needed for production ML serving—all from your existing dbt model:

You just went from dbt model to production ML features without rewriting a single line of transformation logic. Your dbt model—with all its carefully crafted SQL, documentation, and testing—is now:

- **"We stopped rewriting features twice"**: Data engineers build once in dbt, ML teams use directly

- **50-70% faster ML deployment**: From dbt model to production features in minutes, not weeks

- **Single source of truth**: When dbt models update, ML features stay in sync

- **Analytics expertise becomes ML expertise**: Your dbt knowledge directly translates to ML feature engineering

You've invested time and care into your dbt models. They're clean, documented, tested, and trusted by your organization. They shouldn't have to be rewritten to power machine learning—they should work as-is.

Feast's dbt integration makes that possible. Your dbt models become production ML features with:

If you're a dbt user who's been asked to "make those models work for ML," this is your answer.