# Training Orchestrator: Unifying Model Training at Yelp

DevFeed: [Training Orchestrator: Unifying Model Training at Yelp](<https://devfeed.tech/articles/training-orchestrator-unifying-model-training-at-yelp-27429.md>)

Original publisher: [Read original article](<https://engineeringblog.yelp.com/2026/07/training-orchestrator-unifying-model-training-at-yelp.html>)

Author: Ying Wang and Nathan Sponberg, Software Engineer

Published: 2026-07-14T00:00:00Z

Content type: article

Language: en

Sources: [Yelp](<https://devfeed.tech/sources/yelp.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Apache Spark](<https://devfeed.tech/topics/spark.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>), [configuration](<https://devfeed.tech/topics/configuration.md>), [configuration-management](<https://devfeed.tech/topics/configuration-management.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [configuration](<https://devfeed.tech/tags/configuration.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [maintenance](<https://devfeed.tech/tags/maintenance.md>), [mlflow](<https://devfeed.tech/tags/mlflow.md>), [model-training](<https://devfeed.tech/tags/model-training.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [reproducibility](<https://devfeed.tech/tags/reproducibility.md>), [spark](<https://devfeed.tech/tags/spark.md>), [tooling](<https://devfeed.tech/tags/tooling.md>)

## AI overview

Yelp's Core Machine Learning Team developed Training Orchestrator to standardize how machine learning teams define and run Spark-based model training. The configuration-driven system addresses duplicated code, inconsistent configurations, limited local testing, scattered validation and monitoring, and poor reproducibility across environments.

## Source excerpt

At Yelp, we train many machine learning models on different schedules. Applied machine learning teams all have their own set of Spark-based training batches, scripts, and configurations. Over time, these diverged, leading to duplicated code, subtle inconsistencies, and a growing maintenance burden. Yelp's Core Machine Learning Team has developed excellent tooling across our ML ecosystem over the years: feature stores for reproducible data, a unified training library for neural networks and gradient-boosted trees, seamless Spark integration, and MLflow services for model tracking and deployment. But there was still one key piece missing right in the middle: a standardized way to...