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v0.20.x
v0.20.x
  • Introduction to AI Testing
  • Welcome to Distributional
  • Motivation
  • What is AI Testing?
  • Stages in the AI Software Development Lifecycle
    • Components of AI Testing
  • Distributional Testing
  • Getting Access to Distributional
  • Learning about Distributional
    • The Distributional Framework
    • Defining Tests in Distributional
      • Automated Production test creation & execution
      • Knowledge-based test creation
      • Comprehensive testing with Distributional
    • Reviewing Test Sessions and Runs in Distributional
      • Reviewing and recalibrating automated Production tests
      • Insights surfaced elsewhere on Distributional
      • Notifications
    • Data in Distributional
      • The flow of data
      • Components and the DAG for root cause analysis
      • Uploading data to Distributional
      • Living in your VPC
  • Using Distributional
    • Getting Started
    • Access
      • Organization and Namespaces
      • Users and Permissions
      • Tokens
    • Data
      • Data Objects
      • Run-Level Data
      • Data Storage Integrations
      • Data Access Controls
    • Testing
      • Creating Tests
        • Test Page
        • Test Drawer Through Shortcuts
        • Test Templates
        • SDK
      • Defining Assertions
      • Production Testing
        • Auto-Test Generation
        • Recalibration
        • Notable Results
        • Dynamic Baseline
      • Testing Strategies
        • Test That a Given Distribution Has Certain Properties
        • Test That Distributions Have the Same Statistics
        • Test That Columns Are Similarly Distributed
        • Test That Specific Results Have Matching Behavior
        • Test That Distributions Are Not the Same
      • Executing Tests
        • Manually Running Tests Via UI
        • Executing Tests Via SDK
      • Reviewing Tests
      • Using Filters
        • Filters in the Compare Page
        • Filters in Tests
    • Python SDK
      • Quick Start
      • Functions
        • login
        • Project
          • create_project
          • copy_project
          • export_project_as_json
          • get_project
          • get_or_create_project
          • import_project_from_json
        • Run Config
          • create_run_config
          • get_latest_run_config
          • get_run_config
          • get_run_config_from_latest_run
        • Run Results
          • get_column_results
          • get_scalar_results
          • get_results
          • report_column_results
          • report_scalar_results
          • report_results
        • Run
          • close_run
          • create_run
          • get_run
          • report_run_with_results
        • Baseline
          • create_run_query
          • get_run_query
          • set_run_as_baseline
          • set_run_query_as_baseline
        • Test Session
          • create_test_session
      • Objects
        • Project
        • RunConfig
        • Run
        • RunQuery
        • TestSession
        • TestRecalibrationSession
        • TestGenerationSession
        • ResultData
      • Experimental Functions
        • create_test
        • get_tests
        • get_test_sessions
        • wait_for_test_session
        • get_or_create_tag
        • prepare_incomplete_test_spec_payload
        • create_test_recalibration_session
        • wait_for_test_recalibration_session
        • create_test_generation_session
        • wait_for_test_generation_session
      • Eval Module
        • Quick Start
        • Application Metric Sets
        • How-To / FAQ
        • LLM-as-judge and Embedding Metrics
        • RAG / Question Answer Example
        • Eval Module Functions
          • Index of functions
          • eval
          • eval.metrics
    • Notifications
    • Release Notes
  • Tutorials
    • Instructions
    • Hello World (Sentiment Classifier)
    • Trading Strategy
    • LLM Text Summarization
      • Setting the Scene
      • Prompt Engineering
      • Integration testing for text summarization
      • Practical considerations
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  1. Learning about Distributional
  2. Reviewing Test Sessions and Runs in Distributional

Notifications

Customize how you want to be alerted to new Runs, new Test Sessions, and high severity failures

PreviousInsights surfaced elsewhere on DistributionalNextData in Distributional

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Distributional analyzes your data and executes your tests, but we also want to alert you about the status of your tests and the health of your app as new data arrives. We provide a notifications suite, to enable you to receive updates about your app’s health as you desire.

Notifications are configured as part of a Project, as seen in the screenshot above. When a test session fails by some predefined margin (e.g., more than 20% failures), members of your team can receive an alert, such as a Pagerduty. Or, if certain tests concern only certain members of your team, notifications to those members can be limited to only those tests failing.

Learn more about notifications .

here
Customize your notifications at the Project Detail page.