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Censius

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Pricing
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Free
60.9K
#29
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Censius is an AI Observability Platform that helps enterprises monitor, analyze, and explain their production models, improving model velocity and quality

FEATURES

  • 🤖 Seamless integration with Java & Python SDKs or REST API, allowing for deployment on cloud or on-premise platforms.
  • 📚 A user-friendly interface for registering models, logging features, and capturing predictions in just a few lines of code.
  • 🔄 A wide range of monitor configurations to track the entire machine learning pipeline.
  • 📈 Real-time observability for machine learning engineers to detect and analyze model drifts.
  • 🤝 A platform that supports both cloud and on-premise deployments, catering to diverse user needs.

USE CASES

  • 📊 Model Drift Detection: Censius helps detect subtle changes in input patterns for computer vision solutions used in claim routing, flag issues when image data shows signs of poor quality or drift, and explain model predictions for critical data points to customers and internal stakeholders

ADDITIONAL INFO

Release
2020
Location
USA
Employees
11 - 50
Platforms
Laptop

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FAQ

CensiTrac is a cloud-based surgical instrument and asset management system that tracks procedure and patient level. It provides complete visibility into SPD productivity and quality, enabling teams to track surgical inventory to the instrument level.
Censius blog provides a mini guide to MLOps processes and tools. It explains why you would need MLOps, what processes and tools will help you realize it, and how observability can optimize performance and data quality.
Censius is an AI observability platform that provides customized dashboards to quantify the ROI of ML models. It enables real-time collaboration on a unified platform and offers 360-degree view dashboards with reports for stakeholders.
To use Censius, you can seamlessly integrate it through Java & Python SDKs or REST API and deploy it on cloud or on premise. You can register model, log features, and capture predictions in just a few lines of code. Then, choose from dozens of monitor configs to track the entire ML pipeline.

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