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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.

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Construction-site analysis, reducing model training costs

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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.

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Manufacturing, automated product labeling for simplifying object sorting process

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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:

  • 3x faster in model training

  • Demonstrated STADLE performance on extremely small hardware 

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AI Efficiency Redefined with STADLE.

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