How to source training data for your computer vision model: what works, when, and why
In this webinar, we’ll show you how to pick the right training data for your Vision AI project. Real examples, decision tools, and practical tips will help you make choices that actually work so your models perform in the real world, not just on paper.
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Getting a computer vision model to perform well in the real world starts with one key thing: having the right training data. But finding data that is accurate, reliable, and scalable is one of the biggest challenges for data scientists today.
Our webinar covers how to get the right data to train Vision AI models that deliver results. Stan Kazior, Head of Marketing at SKY ENGINE AI, and Patryk Kowalczyk, Python Engineer, will guide you through the main ways to source training data and help you understand which approaches work best for building high-performing computer vision models.
- Review the training data sources available in 2026 and discuss which ones work best under different conditions;
- Share a practical decision matrix to help choose the most suitable data source for your project;
- Compare some data source options using real case studies in face detection, gaze estimation, and facial keypoint recognition;
- Demonstrate the Synthetic Data Cloud by retraining Vision AI models on synthetic data and comparing results against models trained solely on real-world data;
- Provide an honest assessment of when synthetic data excels and when it falls short.
At the end of the webinar, everyone attending can ask questions and discuss topics directly with the SKY ENGINE AI experts.
When: February 12, 3:00 PM CET
Who it’s for: Data Scientists, Data Engineers, Computer Vision Engineers, Machine Learning Scientists/Engineers/Leads, Heads/Directors of Product, Product Managers, AI Scientists, AI Researchers.
Stan Kazior | Patryk Kowalczyk |
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Getting a computer vision model to perform well in the real world starts with one key thing: having the right training data. But finding data that is accurate, reliable, and scalable is one of the biggest challenges for data scientists today.
Our webinar covers how to get the right data to train Vision AI models that deliver results. Stan Kazior, Head of Marketing at SKY ENGINE AI, and Patryk Kowalczyk, Python Engineer, will guide you through the main ways to source training data and help you understand which approaches work best for building high-performing computer vision models.
- Review the training data sources available in 2026 and discuss which ones work best under different conditions;
- Share a practical decision matrix to help choose the most suitable data source for your project;
- Compare some data source options using real case studies in face detection, gaze estimation, and facial keypoint recognition;
- Demonstrate the Synthetic Data Cloud by retraining Vision AI models on synthetic data and comparing results against models trained solely on real-world data;
- Provide an honest assessment of when synthetic data excels and when it falls short.
At the end of the webinar, everyone attending can ask questions and discuss topics directly with the SKY ENGINE AI experts.
When: February 12, 3:00 PM CET
Who it’s for: Data Scientists, Data Engineers, Computer Vision Engineers, Machine Learning Scientists/Engineers/Leads, Heads/Directors of Product, Product Managers, AI Scientists, AI Researchers.
Stan Kazior | Patryk Kowalczyk |