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OctaiPipe 2.2.12 documentation
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OctaiPipe 2.2.12 documentation

Contents:

  • OctaiPipe Getting Started Guide
    • Quickstart
    • Installing OctaiPipe
    • Federated Learning
      • Custom PyTorch Model
      • Custom SGD Model
      • Federated XGBoost Implementation with OctaiPipe
      • FL Train Step
      • Strategy
    • Data Loading and Writing Utilities
      • Influx Flat Converter
    • OctaiPipe Steps
      • Preprocessing Step
      • Feature Engineering Step
      • Model Training Step
      • Model Evaluation Step
      • Model Inference Step
      • WASM Model Inference Step
      • Data (Concept) Drift Monitoring Step
      • Feature Selection Step
      • AutoML in OctaiPipe
        • Setting the RUL clipping level
      • Custom Pipeline Steps
        • Custom pipeline step
    • OctaiPipe Models
    • Edge Device Integration
      • Device Configuration
        • Register a Device
      • Register a Device
    • Running OctaiPipe Pipelines in Kubeflow
    • OctaiKube
      • Ad-Hoc Code running in KubeFlow
    • Develop with OctaiPipe
    • Installing OctaiPipe from the Azure Marketplace
    • Cloud Deployment
      • Deploying to Cloud
      • Step Deployment
      • Generic Cloud Deployment
      • Inference API Deployment
      • InfluxDB Deployment
      • Grafana Deployment
    • Tutorials
      • End-to-end cloud training to edge deployment example - Remaining Useful Life
      • End-to-end cloud training to edge deployment example - classification
      • Example Custom Pipeline Steps
      • Remaining Useful Life Estimation
      • Tutorial for Using MLOps global and local policies
      • Federated Learning - Anomaly Detection
      • Tutorial - Running FL XGBoost with OctaiPipe
      • Tutorial - Running ML Inference with OctaiOxide (OctaiPipe in WASM)
    • Frequently Asked Questions
    • Release Notes
  • Installing OctaiPipe
  • Quickstart
  • Federated Learning
    • Custom PyTorch Model
    • Custom SGD Model
    • Federated XGBoost Implementation with OctaiPipe
    • FL Train Step
    • Strategy
  • Data Loading and Writing Utilities
    • Influx Flat Converter
  • OctaiPipe Steps
    • Preprocessing Step
    • Feature Engineering Step
    • Model Training Step
    • Model Evaluation Step
    • Model Inference Step
    • WASM Model Inference Step
    • Data (Concept) Drift Monitoring Step
    • Feature Selection Step
    • AutoML in OctaiPipe
      • Setting the RUL clipping level
    • Custom Pipeline Steps
      • Custom pipeline step
  • OctaiPipe Models
  • Model and Preprocessor Object Management
  • Edge Device Integration
    • Device Configuration
      • Register a Device
    • Register a Device
  • Running OctaiPipe Pipelines in Kubeflow
  • Develop with OctaiPipe
  • Installing OctaiPipe from the Azure Marketplace
  • User Interface
  • OctaiKube
    • Ad-Hoc Code running in KubeFlow
  • Cloud Deployment
    • Deploying to Cloud
    • Step Deployment
    • Generic Cloud Deployment
    • Inference API Deployment
    • InfluxDB Deployment
    • Grafana Deployment
  • Tutorials
    • End-to-end cloud training to edge deployment example - Remaining Useful Life
    • End-to-end cloud training to edge deployment example - classification
    • Example Custom Pipeline Steps
    • Remaining Useful Life Estimation
    • Tutorial for Using MLOps global and local policies
    • Federated Learning - Anomaly Detection
    • Tutorial - Running FL XGBoost with OctaiPipe
    • Tutorial - Running ML Inference with OctaiOxide (OctaiPipe in WASM)
  • Infrastructure Cost Management
  • Frequently Asked Questions
  • Release Notes
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Quickstart#

  1. End-to-end training to edge deployment (predicting Remaining Useful Life using regression)

  2. End-to-end training to edge deployment (predicting probabilistic Remaining Useful Life using classification)

  3. Example Custom Pipeline Steps

Next
Installing OctaiPipe
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OctaiPipe Getting Started Guide
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