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Computer vision is one of the fastest-growing AI fields. This has been fuelled by the progress made in data models training and its widespread adoption. Automation that results from this increases the quality of products and lowers the cost of production.
In today’s episode, I’m talking to Shahar Zuler, a data scientist and machine learning engineer at Siemens. We'll discuss object recognition in factories and the unique challenges being faced in its deployment.
Tune in and learn more about computer vision in machine learning as well as the use of synthetic data in model training.
Some Questions I Ask:
- How do you see AI impacting the industrial industry? (3:06)
- What are the unique challenges of employing AI/ML in the industrial environment? (10:59)
- What are you doing at Siemens to help solve the industrial environment’s AI/ML challenges? (19:33)
- What do you do to validate the correctness of synthetic data? (23:15)
- Can you predict what you think will happen with machine learning in the next 10 years? (26:57)
In this episode, you will learn:
- Different tasks of computer vision machine learning (11:30)
- How to train an object detection model (16:34)
- How synthetic images are used in ML model training (20:56)
- How to validate synthetic data (23:38)
- The benefits of partnerships between Siemens and their customers (25:08)
Connect with Shahar Zuler:
Connect with Thomas Dewey:
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سلب مسئولیت: پادکست و آثار هنری تعبیه شده در این صفحه متعلق به Siemens Digital Industry Software است که متعلق به صاحب آن است و به Listen Notes، Inc وابسته یا تایید نشده است.
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