Unlocking AI's Full Potential: Performance, Responsibility and Enhancement
Improve RAG Efficiency, Identify Subpar UX Due to Model Inconsistencies, Enhance LLM Prompt Utilization, and Monitor Real-Time Structured and Unstructured Data for Changes.
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
Generative AI Monitoring
Monitor unstructured model issues to proactively troubleshoot and optimize for peak performance.
Deep dive into model behavior with embedding visualization
Maintain the performance of your powerful models with timely detection of data quality issues
Perform root cause analysis behind model predictions with reasoning for negative feedback
Build reliable, transparent, and high-performing models that align with business KPIs
Learn about Generative AI monitoringModel Monitoring
Resolve model staleness and scale model performance monitoring with actionable insights.
Monitor dozens of ML vitals to instantly fix performance issues
Send real-time alerts for threshold violations
Get real-time model performance data
lncrease ROI and minimize operational expenditure
Learn about model monitoringExplainability
Explain complex model predictions and establish trust with model governance and fairness metrics.
Explain the ‘why’ behind black box AI decisions to stakeholders
Perform root cause analysis with global, local, and cohort explainability
Enable bias detection with a wide array of model fairness metrics
Compare model iterations with reasoning to foster decision clarity
Learn about model explainabilityCensius Analytics
Leverage a centralized platform to gauge model performance and its impact on business metrics
Get real-time model performance data
Quantify the ROI of ML models with customized dashboards
Enable real-time collaboration on a unified platform
360-degree view dashboards with reports for stakeholders
Learn about Censius Analytics
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
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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