Webinar, Deep Learning

5 steps to get started with AI in machine vision

Learn how to bring deep learning into real machine-vision applications with a clear, practical roadmap. Since 2017, MVTec has delivered production-ready AI that runs reliably on the factory floor.

Getting started with AI in machine vision can seem complex: Which applications are suitable? How much image data is required? And how can an initial AI model be transferred into a reliable production environment?

In this on-demand webinar, MVTec Product Manager Ulf Schulmeyer presents a clear and practical five-step approach to implementing deep learning in machine vision. You will learn how to evaluate potential applications, reduce project risks, and achieve useful results faster.

What you’ll learn

The five essential steps for implementing deep learning in machine vision.
How to identify suitable AI-based machine vision applications.
What to consider when collecting and preparing image data.
Industrial examples and best practices from real production environments.
How no-code tools can help you build and evaluate your first AI model.

Who should watch?

Machine vision, automation, and PLC engineers.
Quality and production managers looking to improve yield and reduce scrap.
Operations leaders and decision-makers evaluating the industrial use of AI.
Teams planning their first deep learning project in machine vision.

No extensive previous experience with deep learning is required.

About the speaker

Ulf Schulmeyer, Dipl.-Ing., is Product Manager for MVTec MERLIC with 20+ years of experience turning complex technologies into intuitive industrial solutions.

Watch the webinar on demand

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