How deeply does Financial AI in Practice cover Evidently AI?

Summarizes overall drift and provides per-feature and target-specific insights through interactive dashboards.

Evidently AI

As explained in Financial AI in Practice

Evidently is an open-source tool for evaluating and monitoring machine-learning data and models.

Evidently is a general-purpose library for monitoring credit-risk models. Its interactive dashboards highlight data drift—changes in feature distributions—and target drift, such as changes in default rates. section 6.2

Its reports support risk-management review by:

Summarizing overall drift metrics and providing feature-specific and target-specific insights. section 6.2
Offering a general-purpose alternative for risk teams that prefer visual drift reporting alongside tools such as ScorecardMonitoring. section 6.2

How Financial AI in Practice covers it

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Also explained in

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Machine Learning Platform EngineeringEvidently is an open-source tool for evaluating, testing, and monitoring machine-learning models and data.
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Machine Learning System DesignEvidently AI is an open-source Python library for evaluating, testing, and monitoring machine learning models and data.

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Related concepts

data driftuses · Data drift is a change over time in the statistical distribution or characteristics of data relative to a reference distribution or set of characteristics.
monitoringuses · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
reference datasetuses · A reference dataset is a collection of observations designated as a basis for comparison, assessment, or analysis of other data.
population stability indexmeasures · The population stability index (PSI) is a measure of the difference between two probability distributions, often used to quantify shifts in a population or model inputs.
ScorecardMonitoringcontrasts with · ScorecardMonitoring is a scorecard-monitoring component that evaluates changes in score distributions and related statistical indicators.

This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.