Recycleye Enhances its MLOps with Tenyks

Tenyks’ advanced data and model visibility platform provided Recycleye with insights on the dataset and model behaviour that enabled their ML team to deploy the best-performing models into production.
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Recycleye Enhances its MLOps with Tenyks
Using AI-powered innovation to pick plastics, paper, cartons and aluminium to detect and recover more valuable materials.
Industry
Waste Management
HEADQUARTERS
London, United Kingdom
features
Embeddings for Outliers
Enhance Data Quality
Model Comparison
faster model deployment into production
8X
faster model deployment into production
higher model accuracy and reliability on underrepresented classes
33%
higher model accuracy and reliability on underrepresented classes
faster resolution time of model performance discrepancy
88%
faster resolution time of model performance discrepancy

Case Study Preview

Revolutionizing MLOps, Recycleye collaborates with Tenyks, a game-changer in AI. Using NVIDIA TAO Toolkit and Tenyks’ platform, they enhance model performance and edge AI deployment efficiency. The alliance achieves an impressive 8X faster deployment of reliable models, resulting in a remarkable 6.2% performance boost, equivalent to £56.2 Million in increased model value. Tenyks’ centralized dashboard offers unparalleled insights, empowering ML teams to analyze and compare models across diverse environments. Collaboration among ML, Hardware, and Data Engineers fosters synergy, expediting the identification of pivotal pipeline configurations. Continuous monitoring with Tenyks ensures consistent high-quality performance, bolstering customer confidence. Recycleye’s data-centric approach complements its model-centric ethos, solidifying its reputation as a trailblazer in sustainable waste management. Join Recycleye and Tenyks to revolutionize MLOps and unlock the potential of AI for a greener world.

Peter Hedley
Peter Hedley
Chief technology Officer , Recycleye
“Working with Tenyks has been a game changer for us. It has automated a previously laborious process, freeing up our team to focus on more strategic activities. It is awesome to tackle a bug we have been trying to squash for ages, and finally move on without the stress of production models not performing exactly as we expect!”

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