![]() In this blogpost, I’ll discuss how we architected our model training and serving systems into a single, unified model platform. The approach has worked well, and allowed teams to migrate portions of their workflows on to Core ML tooling while leaving other specialized aspects of their domain on legacy systems as needed. By consolidating systems we could increase system efficiency to provide a more robust platform, with tighter SLOs and lower costs.Ĭonsolidating systems for a topic as broad as ML is daunting, so we began by deconstructing ML systems into three main themes and developed solutions within each: interactive computing, data ETL, and model training/serving.By staffing our Core ML team with infrastructure engineers, we could provide new cutting edge capabilities that ML engineers might lack expertise to create or maintain.Centrally managed systems for ML workflows would enable ML developers to focus on the product and ML aspects of their project without getting bogged down by infrastructure.The operational burden of maintaining these systems took a heavy toll, and drew ML engineers’ focus away from modeling iterations or product applications.Ī few years ago, Yelp created a Core ML team to consolidate our ML infrastructure under centrally supported tooling and best practices. Over several years, each system was gradually extended by its team’s engineers to address increasingly complex scope and tighter service level objectives (SLOs). Owning an ML model was a heavy investment both in terms of modeling, as well as infrastructure maintenance. These systems were tailored towards the challenges of their own domains, and cross pollination of ideas was infrequent. Yelp’s first ML models were concentrated within a few teams, each of whom created custom training and serving infrastructure. We have a series of blog posts lined up to discuss the technical details of each component in greater depth, so check back regularly! Yelp’s ML Journey ![]() In this initial blog post, we will be focusing on the motivations and high level design. ![]() Today, we’re announcing our ML Platform, a robust, full feature collection of systems for training and serving ML models built upon open source software. Today there are hundreds of ML models powering Yelp in various forms, and ML adoption continues to accelerate.Īs our ML adoption has grown, our ML infrastructure has grown with it. In the early days of Yelp circa 2004, engineers painstakingly designed heuristic rules to power recommendations like these, but turned to machine learning (ML) techniques as the product matured and our consumer base grew. Inferring possible service offerings so business owners can confidently and accurately represent their business on Yelp.Identifying the most popular dishes for you to try at those restaurants.Helping you discover which restaurants are open for delivery right now.Finding you immediate quotes for a plumber to fix your leaky sink.To connect our consumers with great local businesses, we make millions of recommendations every day for a variety of tasks like: Understanding data is a vital part of Yelp’s success.
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