Cas d'usage/Altaroad
Altaroad

How Altaroad Saved 50% Time Operating Models in Production

From maintenance burden to innovation focus: Altaroad reduced data scientist time on model maintenance from 50% to just 10%.

Obtenir des résultats similaires
50%
Time Saved
Operating models
4x
Faster Deployment
Building & deploying
150K+
Data Points
Managed on-platform
Entreprise
Altaroad
Construction & Waste Monitoring
www.altaroad.com
Présentation

Altaroad is a waste and material monitoring leader providing automated traceability solutions for construction and industrial sites. They use Computer Vision to track material and waste flows via heavy goods vehicles, ensuring compliance and optimizing logistics.

01 — Le Défi

Altaroad needed to develop and deploy multiple computer vision models while managing continuous improvements under time constraints for major contracts.

Multiple Model Types

Needed to develop and deploy classification, object detection, and segmentation models simultaneously for different use cases.

Major Contract Deadlines

Time constraints for a major contract opportunity required dramatically accelerated model development.

Maintenance Burden

Data scientists spent 50% of their time maintaining existing models instead of building new capabilities.

Software Adaptation

Complexity in adapting existing software infrastructure to CV operations created friction.

02 — La Transformation

Des défis aux solutions

Avant Picsellia
  • 50% of data scientist time on maintenance
  • Complex model management across sites
  • Slow adaptation to new CV operations
  • Difficult team onboarding
  • Limited product expansion capability
Après Picsellia
  • Only 10% time on maintenance
  • Efficient and structured monitoring
  • Rapid model development cycles
  • Quick team member onboarding
  • Diversified product offerings
Picsellia allows us to monitor all our models in production in an efficient and structured way, while constantly improving their performances to deliver the most reliable and efficient models to our clients.
Y
Younes
CV Expert, Altaroad
03 — Le Workflow

Comment Altaroad utilise Picsellia

01
Collect Site Data

Aggregate images from construction and industrial sites into organized datasets

02
Train Multiple Models

Develop classification, detection, and segmentation models in parallel

03
Automated Deployment

Ship models to production with automated pipelines

04
Continuous Monitoring

Track performance across all sites with proactive alerts

04 — La Solution

Ce que Picsellia a apporté

Picsellia provided Altaroad with an end-to-end platform that streamlined their entire CV operations from development to production.

End-to-End Platform

AI Laboratory

Complete Computer Vision platform for streamlined operations. From data collection to production monitoring in one place.

4x
Faster deployment

Efficient Model Management

Model Monitoring

Deployment and management capabilities that reduced maintenance time from 50% to 10% of data scientist effort.

40%
Time recovered

MLOps Workflow Automation

Automated Pipelines

Automated pipelines for continuous training and deployment. Models improve automatically as new data arrives.

Collaborative Workspace

Dataset Management

Unified environment where business and technical teams can work together effectively.

150K+
Data points managed
05 — Les Résultats

Impact business

Altaroad transformed their operations and secured market leadership through efficient CV model management.

50% Time Saved

Data scientist time on maintenance reduced from 50% to 10%, freeing up resources for innovation.

Major Contracts Secured

Accelerated development cycles enabled Altaroad to meet critical contract deadlines.

Rapid Team Onboarding

New team members can quickly get up to speed with the unified platform.

Revenue Diversification

Increased product offerings through faster model development capabilities.

Prêt à obtenir des résultats similaires ?

Découvrez comment Picsellia peut aider votre équipe à construire et déployer des modèles de vision par ordinateur plus rapidement.