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Tiger Analytics - MLCore Monitoring Platform
Model Monitoring and Observability Platform
Model Monitoring and Observability Platform
With the rapid increase in the number of ML Assets in Deployment, there is a need for real-time Observability and Performance Monitoring of ML Assets in Production. Data Science Teams look for frameworks that can significantly automate and reduce the overheads to Monitor and Maintain ML Models in Production. A well-maintained ML portfolio often leads to better performance and lower costs for the Enterprise.
Tiger MLCore Monitoring Platform enables ML Observability and ML Monitoring with a single pane of glass view of ML assets across the enterprise. The Platform provides end-to-end visibility of rich metrics across Data Quality, Pipeline Performance, Data Anomalies, Data Health and Characteristics, Model Performance, and Data Drift with rich Explainability features to support Root cause analysis.
The Monitoring platform allows Data Science teams to maintain Models in Production more effectively and generate better ROI for their ML applications.
The accelerator has been successfully deployed for clients across Industries including Consumer Packaged Goods (CPG), Manufacturing, and Energy, across leading cloud providers such as Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS).
With the rapid increase in the number of ML Assets in Deployment, there is a need for real-time Observability and Performance Monitoring of ML Assets in Production. Data Science Teams look for frameworks that can significantly automate and reduce the overheads to Monitor and Maintain ML Models in Production. A well-maintained ML portfolio often leads to better performance and lower costs for the Enterprise.
Tiger MLCore Monitoring Platform enables ML Observability and ML Monitoring with a single pane of glass view of ML assets across the enterprise. The Platform provides end-to-end visibility of rich metrics across Data Quality, Pipeline Performance, Data Anomalies, Data Health and Characteristics, Model Performance, and Data Drift with rich Explainability features to support Root cause analysis. Read More
The Monitoring platform allows Data Science teams to maintain Models in Production more effectively and generate better ROI for their ML applications.
The accelerator has been successfully deployed for clients across Industries including Consumer Packaged Goods (CPG), Manufacturing, and Energy, across leading cloud providers such as Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS).
Enterprise-level Deployment Dashboard covering all ML Assets developed across Cloud Platforms and teams with Performance Monitoring.
Automatic Performance Monitoring for Registered Models across a wide range of Model Performance metrics.
Drift Detection supported by Multiple algorithms with options for custom Thresholds.
Strong Model and Monitoring Explainability features to help data scientists understand the root cause of drift and performance degradation.
Customized ML Dashboards to easily analyze the consolidated performance of all ML Assets and identify degradation across a host of metrics.
Allows the platform to connect to Legacy Model Data Outputs and enable monitoring for Legacy Models.
Create and Configure custom Alerting rules customizable at the Model, Pipeline, and Experiment levels to ensure the highest observability and performance monitoring level.
The Platform is fully open source and can be customized for your enterprise requirements.
Platform comes with end-to-end self-service features allowing Data Scientists and ML Engineers to easily enable comprehensive tracking and monitoring for their projects with minimal configuration.
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