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2.4.1 Release
2.4.1 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
    • Getting Started
    • Tutorials
      • A/B Analysis for a Recommendation Model
      • Using Deployment Configurations
      • Train & Deploy Census Income Classification Model
      • Using Automatic Outlier Detection to find anomalies
      • Monitoring Anomalies with a Custom Metric
      • Monitoring External Models
    • How-To
      • Invoke applications
      • Write definitions
      • Develop runtimes
      • Use private pip repositories
  • 💧Resources
    • Troubleshooting
    • Reference
      • Runtimes
    • Contribution
      • Contributing Pull Requests
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  1. About Hydrosphere
  2. Key Features

Inference Pipelines

PreviousModel RegistryNextA/B Model Deployments

Last updated 4 years ago

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A Hydrosphere user can create a linear inference pipeline from multiple model versions. Such pipelines are called .

🌊
Applications
Inference Pipeline with two stages