Keven Wang

Competence Lead—Machine Learning Engineer, H&M

With 15+ years of experience, Keven becomes a specialist in AI and Data. Besides hands on experience, Keven also has taken various technical leader roles, helping different organization to build AI and data capability, establish tech foundation. Currently Keven is competence lead and also AI architect in H&M group, manages a group of machine learning engineers, also responsible for engineering and architecture.

Past sessions

Summit Europe 2020 Apply MLOps at Scale

November 17, 2020 04:00 PM PT

This session is continuation of "Automated Production Ready ML at Scale" in last Spark AI Summit at Europe. In this session you will learn about how H&M evolves reference architecture covering entire MLOps stack addressing a few common challenges in AI and Machine learning product, like development efficiency, end to end traceability, speed to production, etc.

This architecture has been adapted by multiple product teams managing 100''s of models across the entire H&M value chain and enables data scientists to develop model in a highly interactive environment, enable engineers to manage large scale model training and model serving pipeline with fully traceability.

The team presenting is currently responsible for ensuring that best practices and reference architecture are implemented on all product teams to accelerate H&M groups' data driven business decision making journey.

Speaker: Keven Wang

Summit Europe 2019 Automated Production Ready ML at Scale

October 16, 2019 05:00 PM PT

In this session you will learn about how H&M have created a reference architecture for deploying their machine learning models on azure utilizing databricks following devOps principles. The architecture is currently used in production and has been iterated over multiple times to solve some of the discovered pain points. The team that are presenting is currently responsible for ensuring that best practices are implemented on all H&M use cases covering 100''s of models across the entire H&M group.
This architecture will not only give benefits to data scientist to use notebooks for exploration and modeling but also give the engineers a way to build robust production grade code for deployment. The session will in addition cover topics like lifecycle management, traceability, automation, scalability and version control.