Accelerating time to action

Defects Detection

How to centralize data from 20+ different plants, manage 100k+ monthly new data smoothly, and bring together 10 Computer Vision engineers across 5 countries.

MANUFACTURING

COMPUTER VISION ENGINEER

POC

Defects Detection
Defects Detection

Context

A leading steel-manufacturing company has a computer vision initiative and a great number of use cases involving defect detection on the production line. Quality assessment is often the primary issue in plants.

Their innovation team is spread across more than 5 R&D centers in Europe and the US, making collaboration and knowledge-sharing key factors for their success.

Defects Detection
why choose us
model monitoring icon

1 single point of truth for every CV engineers.

experiment tracking icon

+10 new models trained monthly decrease time spent by data scientists on low-value tasks.

1 month
From 6 to 1 month
to train a test a new approach

Challenge
1
Complex end-to-end model process to be handled by a small team of data scientists.
2
Facilitate collaboration between people with different deep learning skills.
3
Ease the collaboration between people with different levels of Deep Learning skills.
4
Standardize POC approach to raise development standards.
Solution

Model Library: Leverage prebuilt Computer Vision models to train their first models and assess project feasibility.

Tracking & Versioning: Use Picsellia's tracking and versioning system to share trained models across teams.

SDK: Create development standards with Picsellia’s SDK so everyone works the same way.

Hybrid deployment: Store data and deploy models on their own infrastructure. Leverage the Picsellia GDPR infrastructure for model training.

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Start managing your data the right way.

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