Industrial & Manufacturing
Industrial robotics, production ready AI
The Problem:
Training is too slow and keeping models up-to-date and performing well across deployed robots is complex and hard to manage.
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Solution with STADLE:
Train models without having to centralize data. Quickly and securely use new data in your existing models without having to retrain them from scratch.
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Outcomes:
More than 10x Speed in Training, 2.2x accuracy in grabbing objects.
Construction-site analysis, reducing model training costs
Before using STADLE:
Drones gather data that is used to identify critical infrastructure before construction work. Computer vision models are independently trained per instance, resulting in costly and unnecessary redundancy and upkeep.
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After using STADLE :
STADLE’s continuous learning and orchestration capabilities make it easy to add new learnings from multiple instances into a single model. ​ Orchestrated model improved accuracy substantially.
Autonomous Vehicles, optimizing ADAS for drivers with different stress levels
The Problem:
Adapting ADAS (Advanced Driver Assistance Systems) for drivers with different stress levels requires private, efficient and scalable model training.
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Solution with STADLE:
STADLE trains AI models across multiple vehicles in parallel, significantly reducing training time.
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Outcomes:
2.5x faster in training the AI model compared to a single learning environment, optimizing ADAS performance across diverse driving conditions.
Manufacturing, automated product labeling for simplifying object sorting process
The Problem:
Deploying STADLE at scale on existing Edge hardware infrastructure to enable all of STADLE’s benefits
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Solution with STADLE:
STADLE made multiple Edge devices perform continuous learning simultaneously on existing Edge hardware infrastructure to efficiently optimize models.
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Outcomes:
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3x faster in model training
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Demonstrated STADLE performance on extremely small hardware