Björn has 20 years professional experience from data & analytics, mostly from financial markets where he has been working in both in trading, asset management and risk management. In the late 1990’s Björn discovered the joy of working with (3 layer deep) artificial neural nets and developed a model for low latency calculations of optimal hedge ratios for exotic option. In the early 2000’s Björn worked in Asset Management and Risk Management where he worked on unsupervised methods for optimal asset allocation strategies and asymptotic methods for approximating exotic options. After a few years working as a data science consultant he joined a start-up high frequency trading firm developing models for low latency trading on the US exchanges. Björn later joined Nasdaq to serve as the European head of Economic & Statistical Research for 8 years. The past 18 months, Björn has been the Lead Data Scientist focusing on Customer AI at H&M, finally able to join his high school sweat heart, the neural net, who now has grown up to become the sought after companion of every forward leaning corporate leader.
May 26, 2021 11:30 AM PT
This session is a continuation of “Apply MLOps at Scale” at Data+AI Summit Europe 2020 and “Automated Production Ready ML at Scale” at Spark AI Summit at Europe 2019. In this session you will learn how H&M is continuing to evolve and develop their AI platform in order to democratize and accelerate AI usage across the full H&M group, including speed to production, data abstraction, feature store, pipeline orchestration, etc.
Our existing reference 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, enabling engineers to manage large scale model training and model serving pipeline with full traceability. The current evolution aims to both reduce the time to introduce new features to the market as well as the learning feedback loop by democratizing AI in the organisation and persistent focus on sound MLOps principles.