From Theme Parks to Tesla: Building Data Products That Work
A conversation with Abouzar Abbaspour on ML engineering, data pipelines, and real-world impact
What does it take to turn raw data into products that millions of people love to use? Abouzar Abbaspour will discuss this based on his experience working with startups, amusement parks, e-commerce, and now Tesla.
At Efteling theme park, he built queue-time forecasting and recommendation systems for visitors. At bol.com, he helped deploy a recommendation engine to over 6 million users. And today at Tesla, he works on predictive maintenance and integrates LLM agents into production systems.
In this conversation, Abouzar shares stories from the front lines of data and ML engineering: what succeeds, what fails, and what matters when you move from experiments to production.
We plan to cover:
Forecasting queues and building recommendation systems at a theme park
Deploying large-scale recommender systems at bol.com
The leap from data engineering into ML engineering
Predictive maintenance and LLM agents at Tesla
Why productionizing ML is about much more than the model
Which trends in data and ML are hype, and which are here to stay
About the speaker
Abouzar Abbaspour is a machine learning and data engineer whose career spans startups, academia, e-commerce, theme parks, and automotive. He co-founded a telecom startup in Iran, worked on forecasting models and recommendation engines at Efteling, and later deployed large-scale ML models at bol.com. Today, at Tesla, he focuses on predictive maintenance, LLM agents, and scalable pipelines. Abouzar holds an EngD in Data Science from Eindhoven University of Technology.
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