Introduction At Stitch Fix, our data platform is designed to be self-service, with our users taking ownership of their own ETL, models, and microservices. To support this approach, the platform team prioritizes user autonomy and end-to-end tooling in their tooling designs, minimizing the involvement...

In June 2022, Stitch Fix launched the Freestyle search feature, transforming how our clients discover styles that are tailored to their taste. Under the hood, personalized search is fulfilled by a pipeline of modular microservices. We designed the search system to be composable, making it easy to sw...

Stitch Fix uses a cutting-edge multi-tiered recommender system stack to personalize styling recommendations at scale. This stack comprises several critical components, including feature generation, scoring, ranking, and inventory optimization techniques. Our scoring module is based on the Client Tim...

Generative AI has been gaining attention and popularity in recent years. Made possible with advances in deep learning algorithms and trained with previously unimaginable amounts of data, generative AI has already contributed many real-world use cases, from creating realistic images with systems like...

I did a bit of experimenting over the weekend in an attempt to figure out how to write an interface that abstracted over methods whose return type was identical to the receiver’s type. This is frequently a thing that one wants to do when abstracting over self-cloning objects, or things that implemen...

Earlier this year, we had four fantastic interns join our Algorithms team for 3 months to learn how we harness data science in our work at Stitch Fix. These four interns hail from across the country and spent their time exploring a specific project and advancing their skills in their unique interest...

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Introduction The foundation of our recommendation stack is a scoring model we call p(sale), which estimates the probability that any given client will purchase any given item. This model has gone through many iterations over the years, from a mixed effects model, to a matrix factorization model, and...

Title: Large Scale Data & ML Monitoring with whylogs Talk Abstract: In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will,...

Imagine your job is to personalize search results on an e-commerce site for returning customers, classify the presence or absence of pedestrians in street photos, or develop an app that translates languages. In all of these cases, a basic ingredient is a dataset of annotations provided by a human. F...