Why You Need to Incorporate Machine Learning Into Your Search Ranking System

Building a real-time, machine learning-based search-and-ranking system is crucial for e-commerce platforms and other customer-facing applications to drive conversions and enhance user experiences. Traditional search methods often fail to meet users' increasing demands, but machine learning (ML) offers a powerful approach to improving search results by making them contextually relevant and tailored to each user. An ML model can incorporate additional factors and capture a user's taste and intent at the time of the search, personalizing and contextualizing search results. However, implementing such systems in production poses significant challenges, including deploying the right architecture stack to serve predictions at high scale and low latency or combining and serving fresh ML features from various sources to the ranking model. To overcome these complexities, teams are adopting feature platforms like Tecton that help abstract away complexity by managing and orchestrating feature engineering pipelines, continuously computing, storing, and serving feature values to ML models. These platforms enable teams to leverage in-house data to model user preferences, compute and combine real-time, near-real-time, and batch features, serve feature values at high scale and low latency, and limit the amount of engineering effort to build and deploy feature pipelines.