Developing Kubeflow Pipelines:
Kaggle’s H&M Personalized Fashion Recommendations Competition

Recommend products using data from previous transactions on the H&M online store 

Oct 26, 2022 06:00 AM Pacific Time (US and Canada)

Kubeflow familiarity
Beginner to Intermediate

About the Workshop
In this workshop we’ll show you how to turn Kaggle’s H&M Personalized Fashion Recommendations competition into a Kubeflow Pipeline using the KFP SDK and the Kale JupyterLab extension.

About the Kaggle Competition
H&M Group is a family of brands and businesses with 53 online markets and approximately 4,850 stores. Their online store offers shoppers an extensive selection of products to browse through. But with too many choices, customers might not quickly find what interests them or what they are looking for, and ultimately, they might not make a purchase. To enhance the shopping experience, product recommendations are key. More importantly, helping customers make the right choices also has a positive implications for sustainability, as it reduces returns, and thereby minimizes emissions from transportation.

In this competition, H&M Group invites you to develop product recommendations based on data from previous transactions, as well as from customer and product meta data. The available meta data spans from simple data, such as garment type and customer age, to text data from product descriptions, to image data from garment images.

Register Now!

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