Hydrosphere.io
GithubPython SDKContact UsSlack Community
3.0.0 Release
3.0.0 Release
  • Hydrosphere
  • 🌊About Hydrosphere
    • Overview
    • Concepts
    • Platform Architecture
      • Serving
      • Monitoring
      • Interpretability
    • Key Features
      • Model Registry
      • Inference Pipelines
      • A/B Model Deployments
      • Traffic Shadowing
      • Language-Agnostic
      • Automatic Outlier Detection
      • Data Drift Report
      • Monitoring Dashboard
      • Alerts
      • Prediction Explanation
      • Data Projection
      • Kubeflow Components
      • AWS Sagemaker
  • 🏄Quickstart
    • Installation
      • CLI
      • Python SDK
      • Configuring Helm charts
    • Getting Started
    • Tutorials
      • A/B Analysis for a Recommendation Model
      • Using Deployment Configurations
      • Train & Deploy Census Income Classification Model
      • Monitoring Anomalies with a Custom Metric
      • Monitoring External Models
    • How-To
      • Invoke applications
      • Write definitions
      • Develop runtimes
      • Use private pip repositories
  • 💧Resources
    • Troubleshooting
    • Reference
      • Libraries
      • Runtimes
    • Contribution
      • Contributing Pull Requests
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. About Hydrosphere
  2. Key Features

Monitoring Dashboard

PreviousData Drift ReportNextAlerts

Last updated 3 years ago

Was this helpful?

Monitoring Dashboard lets you track your performance and get a high-level view of your data health.

Monitoring Dashboard plots all requests streaming through a model version which are colored in respect with how "healthy" they are. On the horizontal axis we group our data by batches and on the vertical axis we group data by signature fields. In this plot cells are determined by their batch and field. Cells are colored from green to red, depending on the average request health inside the batch.

🌊
metrics
Monitoring Dashboard UI