The AI Observability Platform for Enterprise ML Teams
Get end-to-end visibility of your production models and adopt a proactive approach toward model management to continuously deliver reliable ML.
A single platform for delivering enterprise level observability at scale.
Compare
Compare different model versions
Conduct data & feature quality checks
Validate
Verify model performance using metrics
Derive possible ROI out of ML initiatives
Automate
Automate post production workflow
Collect traffic and metadata logs
Monitor
Continuously monitor models for drifts
Get real time alerts on preferred channels
Explain
Proactively detect suspicious patterns
Explain decisions to customers with clarity
Analyze
Perform root cause analysis
Visualize model performance in dashboards
Censius AI
Observability Platform
Censius offers end-to-end AI observability that delivers automated monitoring and proactive troubleshooting to build reliable models throughout the ML lifecycle
Censius Monitoring
Automate model performance monitoring
Send real time alerts for violations
Increase ROI & reduce resource costs
Learn About MonitoringCensius Explainability
Analyze root causes of model decisions
Reduce model risks and time to resolve issues
Establish trust with model transparency
Learn About ExplainabilityCensius Analytics
Get real time model performance data
Quantify ROI of ML models with dashboards
Share model performance data with other teams
Get started in 3 simple steps
Seamlessly integrate Censius through Java & Python SDKs or REST API and deploy it on cloud or on premise.
- 1
Integrate SDK
Register model, log features and capture predictions in just a few lines of code.
- 2
Set up monitors
Choose from dozens of monitor configs to track the entire ML pipeline.
- 3
Observe
Register model, log features and capture predictions in just a few lines of code.
Observability for Everyone
Machine Learning Engineers
Detect and Analyze Model Drifts
Automate the continous monitoring of models to detect drifts and outliers
Get Root Cause Analysis of Decisions
Dig down on every model decision to identify and study the components that lead to it
Analyze Performance of Cohorts
Slice the data into different cohorts to ensure decision consistency and elimination of bias
Product and Business Stakeholders
Gain end-to-end visibility of model performance
Have complete visibility and understanding of model performance
Build trust with explainability
Create trust among users by enabling model explainability for every decision
Study Business ROI
Understand how a model is adding business value using specific business metrics and visualisation
Data Scientists
Monitor Data Quality
Eliminate missing, unexpected or extreme values to ensure data is consistent across the ML pipeline
.svg)
Understand Feature Distribution
Deep dive into what features are contributing to model performance and improve model output
Compare Model Versions
Evaluate multiple model versions to analyze and identify the best performing ones
What people are saying
Trishtana Banerjee
I found Censius monitors to be thorough and easy to use with a good range of flexibility for both generic and specific monitoring needs. This adds velocity to AI solutions.
Rajat Soni
Censius provides transparency into the model decisions with its monitors and dashboard reports, cutting down the to-and-fro iterations. This saves a lot of time and effort.
Deepak Jhanji
Censius helps know if, when, and why the model has degraded and if it requires training. The continuous monitors are very convenient for clients as well as other stakeholders.
Rishav Ray
Censius has been really well built, and by using this we can definitely monitor all ML-based metrics. I see the value of leveraging it at the tail end of our ML life cycle.