Augmented commerce: Machine learning at Shopify (2025) - Shopify

This post details the application of machine learning across various Shopify domains. It highlights the use of finetuned Qwen multimodal models for product classification and metadata enrichment, involving hundreds of millions of inferences daily. It also mentions fast risk models for fraud detection, inspired by Feature Space work. Sidekick, the merchant assistant, is powered by finetuned LLaMa models and large general models. Nomic embeddings are used for vector representations of billions of products, empowering search and recommendations. A tabular transformer-based model is used for forecasting merchant GMV for cash advance risk assessment. The HSTU architecture is being experimented with for customer and merchant behavior modeling. The search team is exploring query rewriting with small language models. The post also details investments in ML infrastructure, including partnerships with GCP and neo-cloud providers for GPUs, CentML for compute acceleration, and Toloka for data services. Early access to frontier models is utilized for annotations, evaluations, and direct deployments like Sidekick, accelerating prototyping and development.