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v0.24.3
v0.24.3
  • ☂️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
    • Example
      • Authentication & loading data
      • Setting up the model schema
      • Creating the monitoring model
      • Customizing the monitoring model settings
      • Setting up continuous monitoring
      • Add delayed ground truth (optional)
    • 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 0.24.3
      • Version 0.24.2
      • Version 0.24.1
      • Version 0.24.0
      • Version 0.23.0
      • Version 0.22.0
      • Version 0.21.0
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  1. NannyML Cloud SDK
  2. Example

Customizing the monitoring model settings

Now we've created the monitoring model, we can start tweaking its settings before running it.

To do so, we'll fetch the RuntimeConfiguration linked to the monitoring model. It offers methods to easily fetch and modify the configuration entries.

# Fetch the correct RuntimeConfiguration
config = sdk.monitoring.RuntimeConfiguration.get(model_id=model["id"])

# Now we can use this object to manipulate the monitoring model settings.
# Let's enable the Jensen-Shannon Univariate Covariate Shift method for our target values.
config.univariate_drift_method("JENSEN_SHANNON").enable_targets()

# Let's disable the Domain Classifier Multivariate Covariate Shift method
config.multivariate_drift_method("DOMAIN_CLASSIFIER_AUROC").disable()

# Let's set a constant threshold for the Missing Values data quality metric
config.data_quality_metric("MISSING_VALUES").set_threshold(threshold_type="CONSTANT", upper=5)

# Now apply the altered configuration
sdk.monitoring.RuntimeConfiguration.set(model_id=model["id"], config=config)
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