Figure out what's wrong with your model and fix it.
Collaborate to identify and fix dataset issues.
Build robust and reliable models that generalise.
Understand, visualise, and curate datasets to uncover corrupted points, fix incorrect labels, and identify difficult edge cases.
Understand the root causes of common failure patterns. Remove spurious correlations. Reduce unexpected failures by 80% and boost performance by 5%.
Collaborate with team members, discuss datasets and involve different stakeholders.
Reduce the time it takes engineers to understand why a model is behaving strangely.
A medical imaging ML developer identified that 95% of all predictions were invalid because the output depended on the wrong input signal.
Faster model development & debugging.
Reduction in annotation costs.
Decrease in unexpected failures.
Added value from model improvement.
We started Tenyks to invent the way humanity interacts with AI to protect and delight. To protect the world from the misuse of AI. But also to ensure that it is developed with passion, excitement, and joy!
At Tenyks we set our goals ridiculously high and stick together to go further than anything previously imaginable. We start small, work hard, and deliver fast, embracing the inevitable obstacles with open hearts because challenges fuel our burning desire for learning.
Tenyksians make no distinctions between work and play. We simply pursue our vision of excellence, leaving others to decide whether we are working or playing. This is Tenyks.
Tenyks helps ML engineers working with computer vision data to build more reliable software faster!