NannyML Cloud
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v0.24.0
v0.24.0
  • ☂️Introduction
  • Model Monitoring
    • Quickstart
    • Data Preparation
      • How to get data ready for NannyML
    • Tutorials
      • Monitoring a tabular data model
      • Monitoring with segmentation
      • Monitoring a text classification model
      • Monitoring a computer vision model
    • How it works
      • Probabilistic Adaptive Performance Estimation (PAPE)
      • Reverse Concept Drift (RCD)
    • Custom Metrics
      • Creating Custom Metrics
        • Writing Functions for Binary Classification
        • Writing Functions for Multiclass Classification
        • Writing Functions for Regression
        • Handling Missing Values
        • Advanced Tutorial: Creating a MTBF Custom Metric
      • Adding a Custom Metric through NannyML SDK
    • Reporting
      • Creating a new report
      • Report structure
      • Exporting a report
      • Managing reports
      • Report template
      • Add to report feature
  • Product tour
    • Navigation
    • Adding a model
    • Model overview
    • Deleting a model
    • Model side panel
      • Summary
      • Performance
      • Concept drift
      • Covariate shift
      • Data quality
      • Logs
      • Model settings
        • General
        • Data
        • Performance settings
        • Concept Drift settings
        • Covariate Shift settings
        • Descriptive Statistics settings
        • Data Quality settings
    • Account settings
  • Deployment
    • Azure
      • Azure Managed Application
        • Finding the URL to access managed NannyML Cloud
        • Enabling access to storage
      • Azure Software-as-a-Service (SaaS)
    • AWS
      • AMI with CFT
        • Architecture
      • EKS
        • Quick start cluster setup
      • S3 Access
    • Application setup
      • Authentication
      • Notifications
      • Webhooks
      • Permissions
  • NannyML Cloud SDK
    • Getting Started
    • API Reference
  • Probabilistic Model Evaluation
    • Introduction
    • Tutorials
      • Evaluating a binary classification model
      • Data Preparation
    • How it works
      • HDI+ROPE (with minimum precision)
      • Getting Probability Distribution of a Performance Metric with targets
      • Getting Probability Distribution of Performance Metric without targets
      • Getting Probability Distribution of Performance Metric when some observations have labels
      • Defaults for ROPE and estimation precision
  • Experiments Module
    • Introduction
    • Tutorials
      • Running an A/B test
      • Data Preparation
    • How it works
      • Getting probability distribution of the difference of binary downstream metrics
  • miscellaneous
    • Engineering
    • Usage logging in NannyNL
    • Versions
      • Version 0.24.0
      • Version 0.23.0
      • Version 0.22.0
      • Version 0.21.0
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On this page
  • Notifications - alerting report
  • Danger zone: configure application
  1. Product tour

Account settings

PreviousData Quality settingsNextAzure

In account settings, you can opt out of anonymous usage statistics or set up a notifications system via email with reports. The API token section lets you create a token to authenticate when using nannyML Cloud SDK.

Notifications - alerting report

Suppose there are any significant alerts on the last chunk. In that case, NannyML will send an automated email to your email account with some of the main insights: the performance metrics that have dropped and the potential causes of it.

All are linked to the respective dashboards with a full view of the features and metrics with issues.

Danger zone: configure application

In the danger zone you'll find a link to the . It will walk you through the configuration of authentication and notifications of your NannyML Cloud instance. A misconfiguration here might make your instance unavailable for other users, so treat this with care.

application setup wizard
Account settings page.
Alert report sent to the provided email.