Les Fondamentaux de la Qualité des Données pour les Projets IA

Ce qu'elle couvre

Ce cours couvre les défaillances de qualité des données les plus courantes qui compromettent les initiatives IA et fournit des techniques pratiques pour les prévenir. Les participants apprennent à profiler des jeux de données, identifier et corriger les problèmes fréquents (valeurs manquantes, doublons, dérive de schéma, bruit dans les labels) et à construire des pipelines de validation. Le programme combine des cadres conceptuels avec des outils concrets (Great Expectations, tests dbt, Pandas Profiling) pour intégrer des contrôles qualité à chaque étape du cycle de vie des données. Dispensé en format mixte ou en auto-apprentissage, il s'adresse aux analystes et ingénieurs de données travaillant sur des pipelines IA/ML.

À l'issue, vous saurez

• Run a full data quality profile on a new dataset and document its key risks before model training begins
• Identify and remediate at least five common data quality issues (nulls, duplicates, outliers, schema drift, label noise) using Python or SQL
• Write automated data validation tests using Great Expectations or dbt that can be integrated into a CI/CD pipeline
• Design a data quality monitoring dashboard that alerts the team when upstream data deviates from expected distributions
• Conduct a root-cause analysis linking a drop in model performance to a specific upstream data quality issue

Sujets abordés

• Data profiling: distributions, cardinality, nulls, and outlier detection
• Common AI-specific data quality failure modes (label noise, feature leakage, schema drift)
• Data cleaning strategies: imputation, deduplication, standardisation
• Validation frameworks: writing and automating data quality tests with Great Expectations or dbt
• Data lineage and documentation practices for auditability
• Monitoring data quality in production pipelines
• Root-cause analysis when a model degrades due to data issues
• Building a data quality scorecard for ongoing reporting

Modalité

Typically delivered as two or three half-day sessions (in-person or remote) with a 60/40 split between hands-on labs and concept sessions. Participants work on a shared sample dataset as well as optionally their own organisational data. Materials include slide decks, Jupyter notebooks, a Great Expectations starter configuration, and a data quality scorecard template. A follow-up async review session is recommended two weeks post-training.

Ce qui fait que ça marche

• Assigning a named data quality owner per dataset or pipeline domain
• Integrating automated validation tests into CI/CD so failures block bad data from reaching models
• Establishing a shared data quality scorecard reviewed in regular team standups
• Starting with the highest-impact datasets feeding live AI models rather than attempting to fix everything at once

Erreurs fréquentes

• Treating data quality as a one-time pre-processing step rather than a continuous pipeline concern
• Focusing only on completeness (nulls) while ignoring distributional drift and label quality
• Building quality checks in notebooks instead of automating them as part of the data pipeline
• Skipping data quality documentation, making it impossible to audit why a model behaved unexpectedly in production

Fournisseurs à considérer

• DataScientestdatascientest.com →
• Jedha Bootcampwww.jedha.co →
• DataCampwww.datacamp.com →
• dbt Learnlearn.getdbt.com →

Sources

• Great Expectations Documentation — Data Quality for ML Pipelines →
• dbt Data Testing Best Practices →