Welcome to NannyML’s documentation!

Contents:

• Introduction
  • What is NannyML?
  • Key features
  • Next steps
  • Get early access to NannyML Web App
• Installing NannyML
  • Extras
• Quickstart
  • What is NannyML?
  • Exemplary Workflow with NannyML
  • What’s next?
• Tutorials
  • Data requirements
  • Estimating Performance
  • Monitoring Realized Performance
  • Comparing Estimated and Realized Performance
  • Detecting Data Drift
  • Ranking
  • Data Quality Checks
  • Summary Statistics
  • Storing and loading calculators
  • Working with results
  • Adjusting Plots
  • Chunking
  • Thresholds
• How It Works
  • Estimation of Performance of the Monitored Model
  • Business Value Estimation and Calculation
  • Presenting Univariate Drift Detection Methods
  • Choosing Univariate Drift Detection Methods
  • Ranking
  • Multivariate Drift Detection
  • Chunking Considerations
  • Calculating Sampling Error
  • Thresholds
• Examples
  • Binary Classification: California Housing Dataset
  • Full Monitoring Workflow - Regression: NYC Green Taxi Dataset
• Example Datasets
  • US Census Employment dataset
  • Synthetic Binary Classification Car Loan Dataset
  • Synthetic Multiclass Classification Dataset
  • Synthetic Regression Dataset
  • California Housing Dataset
  • Titanic Dataset
• Glossary
• Command Line Interface (CLI)
  • Running the CLI
  • Configuration file
  • Command overview
• Usage logging in NannyML
  • TLDR
  • What do we mean by usage statistics?
  • Why are we doing this?
  • How usage logging works
  • To opt in or not to opt in, that’s the question
  • How to disable usage logging
• API reference
  • nannyml package
• Contributing
  • Spread the word
  • Be a part of the team
  • Contribute to the codebase

Indices and tables

• Index

• Module Index

• Search Page