Is your deployed AI system successful?

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Maarten Van den Broeck

Senior Content Developer at DataCamp

When to measure success?

MLOps methodology lifecycle

When to measure success?

Measuring success across the MLOps lifecycle

Measuring performance offline - accuracy

Training and validation data

Measuring performance offline - accuracy

Training a model

Measuring performance offline - accuracy

Validating a model with test examples

Measuring performance offline - accuracy

Comparing predictions to actual labels

Measuring performance offline - accuracy

Calculating accuracy

Beyond accuracy - error and other metrics

Error metric in regression models

Metrics for search and recommendation engines: ranking quality -relevance of ranking items to the user-, diversity in search results or recommendations, etc.

Ranking of recommended products

Measuring success in production

Model degradation: the measured metric value gets worse over the time

Model degradation

Business metrics: Key Performance Indicators (KPIs)
- Indicator of performance and progress of organization objectives

Risks: what could possibly go wrong?

Possible risks include:

Data bias
Lack of transparency
Ethical concerns
Dubious system reliability
Vulnerability to cyber threats

Proof-of-Concept (PoC):

Pilot demonstrator to validate feasibility and potential value + early risk identification

Let's practice!

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