11-30, 14:10–14:40 (Europe/Amsterdam), Bohr
Working with time series data can be special - so let's talk about things we have learned deploying models dealing with this type of data at scale.
Time series data is ubiquitous in our world today: from sensor data collected by the Internet of Things (IoT) to sensors in manufacturing equipment, from financial records to weather data. As the amount of time series data we collect continues to grow, so too do the challenges of analyzing and solving complex business questions with it.
Time series pose some quite unique challenges that can hit you very hard when you are trying to deploy it to productive use. In this presentation we will highlight some of the most common pitfalls we are recurrently seeing in the field - to spoiler some: time zones, denoising or re-training challenges.
In addition, we will also discuss some of the specifics of doing data science with this type of data at scale (and in production).
No previous knowledge expected
Sebastian is a Principal Data Scientist at Thoughtworks, with over 20 years of experience on the intersection of data, IT, and complex use cases. He has a background in Chemical Engineering and has used Python since the late 90s to automate tedious tasks (or just replace bash / Perl scripts).
Sebastian has led teams of modeling experts and architected machine learning solutions for a variety of clients, including Fortune 500 companies. He is also the founder and former CTO of a spin-off company that developed a SaaS product for data-driven optimization of continuous manufacturing processes. Sebastian exited the company in 2021 and is now passionate about helping other organizations leverage data and technology to solve their most challenging problems.
Alyona Galyeva is a Principal Engineer at Thoughtworks, Microsoft AI MVP, PyLadies Amsterdam organizer, MLOps and Crafts co-organizer.
Observe - Optimize - Learn - Repeat
Passionate about encouraging others to see different perspectives and constructively break the rules.
I found my joy in building, optimizing, and deploying end-to-end AI and Data Engineering Solutions.