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QUICK START

  • Welcome to Wizata!
  • What is Wizata?
    • Who is it for?
    • Why Wizata?
    • Use Cases
  • Architecture
    • Architecture key components and hosting choices
  • Roles and permissions
  • Licensing

DATA HUB

  • What is the Data Hub ?
  • Connect your data to Wizata
    • Time-series data - format & types
    • Stream and loop on a data file through Azure Event Hub
    • Upload a small data file through Azure Event Hub
  • Edge Device
    • Edge Architecture & Requirements
    • Register and setup an Edge device
    • Using Edge to connect your data
    • Troubleshoot your device
  • Data Points
    • Updating your datapoint metadata
    • Categories, units and labels
    • Exploring data
    • Creating datapoints manually
  • Twins
    • Creating and connecting Twin Units
    • Editing twin properties
    • Adding datapoints to twins
    • Tutorial: Create Twin from tags list
  • Query
    • Dynamic Selector
    • Multi-bucket support on queries
  • Events & Batches
    • Design your Event/Batch solution
    • Learn how to upload event data
    • Working with simultaneous event statuses
    • Querying Event datapoints
  • Data Explorer
    • Adding Virtual Data Source to the Explorer
  • Data Stores
  • Tutorial: Twin Structure and Datapoint Properties summary

AI LAB

  • What is the AI Lab ?
  • Tutorials: Use Cases
    • Tutorial: Anomaly Detection Solution
  • Template
    • Managing Business labels
  • Pipeline
    • Working with pipelines
    • Steps
      • Query step
      • Script step
      • Model step
      • Write step
      • Plot step
    • Context of a pipeline
    • Custom configuration
    • Pipeline Images
    • Error Handling
    • Send Alerts by SMS, Emails, Teams, Slack
      • Tutorial: Anomaly Detection - Email & Slack Alerts
    • Wizata library for common functions
  • Models
    • Understanding Model Storage and Metadata in Wizata
    • Uploading a Manually Trained Model
    • Automating Model Training with Pipelines
  • Experiment
  • Trigger
  • Execution
  • Environment Management
    • Import custom packages to the platform
    • Upgrade your solution Python version

CONTROL PANEL

  • What is the Control Panel?
  • Navigation
  • Components
    • Creating a dashboard component
      • Chart widget
      • Sensor widget
      • Gauge widget
      • Python Plotly widget
    • Creating a Grafana Component
    • Creating a Streamlit solution
    • Iframe embedding
  • Interacting with time interval selector
  • Insights
    • Insight conditions and severity levels
  • Control Panel - Mobile Support

INTEGRATION

  • Grafana integration
    • Link your Grafana environment to Wizata
    • Query data from Wizata within Grafana
    • Call a pipeline from a Grafana dashboard
  • Jupyter Notebook
  • ML Flow
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Understanding Model Storage and Metadata in Wizata

This article explains how models are stored, how they are identified, how metadata is managed, and how artifacts are associated with each model version.

Updated about 1 month ago


What’s Next
  • Uploading a Manually Trained Model
  • Automating Model Training with Pipelines