TALKING

Volume , variety and velocity of data is key to training and testing deep learning models , so they deliver outcomes expected when deployed in real life .

A larger , more diverse range of pooled data in a cloudbased platform from across sites and environments is better for deep learning training . Such a platform would allow defined users to work together in real time , collaborate on annotation , training , and testing projects , and share their expertise .

With a cloud-based platform , users with defined roles , rights and responsibilities could train and test deep learning models in the cloud . temperatures , and alterations in production conditions . Failing to account for these changes in your training data can lead to reduced model accuracy .

Each site may introduce variations in sharpness , working distance , ambient light , and other factors that the model will learn to manage , so training datasets reflect the full range of variations that the model may encounter in real-world scenarios . If industrial processes involve multiple production sites , it is a mistake to collect data from only one of them or collect from all of them but keep the data siloed .

To fix this , data should be captured and shared from different environmental conditions and production sites , but how ?

Another issue with a siloed site approach concerns the annotation of training data for deep learning models . Inaccurate , unclear , and inconsistent annotations inevitably lead to models that do not perform well . It is critical to ensure annotations are precise and unambiguous including across production sites making the same items , but this requires teams to be able to collaborate on annotation projects .

Marking different defect types on different images while leaving some defects not marked at all is a common mistake in real-world projects . And what counts as a defect can also be subjective , so crossvalidation is important . All defects , regardless of type , should be clearly marked on all relevant images .

Again , without taking a unified approach , and leveraging the cloud , the challenge of data annotation among sites and countries remains .

Machine vision teams across manufacturing industries need new ways to leverage deep learning machine vision , which should include using the cloud . A cloud-based machine vision platform would allow teams to securely upload , label , and annotate data from multiple manufacturing locations across site , country , and region .

Powered by much better training and testing data , they may deliver much higher levels of visual inspection analysis and accuracy beyond conventional , rulesbased machine vision for certain use cases .

These outcomes are sought by manufacturers in the automotive , electric battery , semiconductor , electronics , and packaging industries , to name a few .

A cloud-based solution also delivers scalability and accessibility of computing power . With traditional systems , some select employees get strong GPU cards in their computers to perform large trainings . With the cloud , every user can access the same high computing power from their laptops .

Some costs are generated , but through a pay-as-yougo subscription model , it may still be more beneficial than investing in a company ’ s own servers and additional hard-to-find IT personnel .

A software as a service model would give machine vision teams the flexibility and ease of investing in a cloud-based platform with a subscription while the technology partner seamlessly adds new features , models , and updates .

Deep learning cloud-based platforms will allow for model edge deployment on PCs and devices to support flexible , digitised workflows on the production line , on a PC or device wherever a user or team is located .

Manufacturing leaders expect AI to drive growth . This surge in AI adoption , combined with leaders prioritising digital transformation , underscores manufacturers ’ intent to improve data management and leverage modern technologies that enhance visibility and quality throughout the manufacturing process .

One of today ’ s most significant quality management issues is integrating data . With AI and data goals and new automotive plants planned , the time is ripe to look at the potential of the cloud to leverage data and extend the benefits of deep learning machine vision . p

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