12-07, 13:30–14:00 (UTC), Machine Learning Track
Explore the labyrinth of hidden technical debt in ML systems through the lens of a data scientist. Delve into six core challenges, illustrated by a churn prediction model case, and discover Python's prowess in navigating these challenges. Uncover Python tools like Docker, Flyte, Airflow, and Git that arm you against technical debt, leading to resilient ML infrastructure.
This talk encapsulates the journey from recognising to rectifying hidden technical debt in ML systems, enriched with real-world examples. It's a blend of theory, practical insights, and a showcase of Python's extensive toolkit to fortify ML infrastructure against inherent challenges, aiming to equip attendees with strategies for building robust, maintainable systems.
No previous knowledge expected
Data-driven leader with over 13 years of experience in consulting and in-house roles across diverse industries like wealth management, mortgage lending, telecom, and streaming services. Adept at using a product-focused approach to deliver business value. Currently leading data and Analytics at Schibsted, I am passionate about leveraging data to drive informed decisions and business growth. Open to opportunities where I can bring my expertise to create impactful data strategies.