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Drishti: Deep Learning for Manufacturing with Krish Chaudhury (Repeat)

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Conteúdo fornecido por Machine Learning Archives - Software Engineering Daily. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Machine Learning Archives - Software Engineering Daily ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Originally published April 17, 2019

Drishti is a company focused on improving manufacturing workflows using computer vision.

A manufacturing environment consists of assembly lines. A line is composed of sequential stations along that manufacturing line. At each station on the assembly line, a worker performs an operation on the item that is being manufactured. This type of workflow is used for the manufacturing of cars, laptops, stereo equipment, and many other technology products.

With Drishti, the manufacturing process is augmented by adding a camera at each station. Camera footage is used to train a machine learning model for each station on the assembly line. That machine learning model is used to ensure the accuracy and performance of each task that is being conducted on the assembly line.

Krish Chaudhury is the CTO at Drishti. From 2005 to 2015 he led image processing and computer vision projects at Google before joining Flipkart, where he worked on image science and deep learning for another four years. Krish had spent more than twenty years working on image and vision related problems when he co-founded Drishti.

In today’s episode, we discuss the science and application of computer vision, as well as the future of manufacturing technology and the business strategy of Drishti.

Sponsorship inquiries: sponsor@softwareengineeringdaily.com

The post Drishti: Deep Learning for Manufacturing with Krish Chaudhury (Repeat) appeared first on Software Engineering Daily.

  continue reading

175 episódios

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iconCompartilhar
 
Manage episode 281061351 series 1433944
Conteúdo fornecido por Machine Learning Archives - Software Engineering Daily. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Machine Learning Archives - Software Engineering Daily ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Originally published April 17, 2019

Drishti is a company focused on improving manufacturing workflows using computer vision.

A manufacturing environment consists of assembly lines. A line is composed of sequential stations along that manufacturing line. At each station on the assembly line, a worker performs an operation on the item that is being manufactured. This type of workflow is used for the manufacturing of cars, laptops, stereo equipment, and many other technology products.

With Drishti, the manufacturing process is augmented by adding a camera at each station. Camera footage is used to train a machine learning model for each station on the assembly line. That machine learning model is used to ensure the accuracy and performance of each task that is being conducted on the assembly line.

Krish Chaudhury is the CTO at Drishti. From 2005 to 2015 he led image processing and computer vision projects at Google before joining Flipkart, where he worked on image science and deep learning for another four years. Krish had spent more than twenty years working on image and vision related problems when he co-founded Drishti.

In today’s episode, we discuss the science and application of computer vision, as well as the future of manufacturing technology and the business strategy of Drishti.

Sponsorship inquiries: sponsor@softwareengineeringdaily.com

The post Drishti: Deep Learning for Manufacturing with Krish Chaudhury (Repeat) appeared first on Software Engineering Daily.

  continue reading

175 episódios

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