Case Study: Sift streamlines ML pipeline orchestration with Apache Airflow

Sift - Customer Case Study

Sift, a company that provides a Digital Trust & Safety platform, faced a challenge in managing its complex machine learning model training pipelines. Their workflows consisted of hundreds of steps in MapReduce and Spark with intricate dependencies. They lacked a centralized way to orchestrate these dependencies and schedule job execution, making it difficult to scale and coordinate multiple experimental workflows. This is where the vendor, Apache Airflow, provided a solution.

By implementing Apache Airflow, Sift could define and organize all job dependencies using Directed Acyclic Graphs (DAGs). The solution enabled them to create isolated environments for experimental pipelines and manage tasks through a central UI for monitoring and retries. As a result, Apache Airflow empowered Sift to move beyond rigid cron jobs, efficiently manage their expanding ML pipelines, and support the creation of entirely new and diverse data workflows.

Sift

Handong Park

Sift