Seasoned software architect with hands on experience in data engineering and machine learning
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.