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.
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.
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.
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.
Solution with STADLE:
STADLE trains AI models across multiple vehicles in parallel, significantly reducing training time.
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
Solution with STADLE:
STADLE made multiple Edge devices perform continuous learning simultaneously on existing Edge hardware infrastructure to efficiently optimize models.
Outcomes:
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3x faster in model training
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Demonstrated STADLE performance on extremely small hardware
