Monitor, explain, and optimize ML models
Proactively detect and resolve performance regression, poor data quality, and model drifts to build reliable machine learning models.
Censius
AI Observability Platform
Censius offers an end-to-end AI observability platform that delivers automated monitoring and proactive troubleshooting to build reliable models.
Censius Monitoring
With automated and continuous monitoring, Censius helps you to scale reliable models even while redirecting your team’s efforts towards more strategic tasks.
Continuously monitor performance, data quality, and model drifts- Automatically detect threshold values
- Send real-time alerts for prioritized violations
Censius Explainability
With precise segmentation and logging capabilities, Censius helps you to analyze the root causes behind every model decision to proactively build improvement strategies
- Compare models across several versions
- Identify data segments to study localized behaviour
- Proactively locate drifts through baseline comparisons
Censius Responsibility
Built for proactive detection and resolution of violations, Censius offers continuous support for building Responsible AI, resulting in minimal impact on end customers.
- Detect and study prediction biases
- Improve model performance for specific cohorts
- Maintain compliance with industry standards
Seamlessly integrates with your existing ML stack
Plug Censius into your current ML workflow easily and quickly
Your All-in-One Monitoring Platform
With Censius’ multi-tier capabilities, eliminate the need for managing a host of tools for AI Observability
New to AI Observability?
Get a quick start with our resources
What is Concept Drift and why does it go undetected?
A walk-through of why you would need MLOps, what processes and tools will help you realize it
Model Monitoring in Machine Learning - all you need to know
What can go wrong with models in production? What needs to be monitored and Why?
What is AI Observability?
AI observability is a holistic and complete approach to drive insights on the model’s behavior, data, and performance across its lifecycle.