Description

This curriculum spans the breadth of a multi-workshop program on data quality for machine learning, comparable to an internal capability build within an enterprise MLOps team, covering the full lifecycle from requirement setting and pipeline validation to governance, cross-system integration, and incident response.

Module 1: Defining Data Quality Requirements for ML Projects

Establishing precision, recall, and latency thresholds based on business SLAs for model performance
Selecting key data dimensions (accuracy, completeness, consistency) based on use case impact analysis
Negotiating data ownership and quality accountability across business units and data engineering teams
Mapping data lineage from source systems to feature stores to identify quality chokepoints
Documenting acceptable data drift thresholds for input features used in real-time inference
Aligning data quality KPIs with model monitoring objectives during the project intake phase
Conducting stakeholder workshops to prioritize data issues affecting downstream decisions
Specifying fallback behaviors when data quality degrades below operational thresholds

Module 2: Data Profiling and Anomaly Detection in Production Pipelines

Implementing statistical baselines (mean, variance, cardinality) for numeric and categorical features
Configuring automated schema validation rules to detect unexpected data types or null rates
Deploying histogram divergence checks (e.g., PSI, CSI) between training and serving data
Setting up outlier detection using IQR or Mahalanobis distance on high-dimensional embeddings
Integrating Great Expectations or TensorFlow Data Validation into CI/CD workflows
Designing sampling strategies for profiling large-scale datasets without performance degradation
Creating alerting thresholds that balance false positives with operational urgency
Logging profiling results to a central data observability platform for auditability

Module 3: Feature Engineering with Quality Constraints

Choosing imputation strategies (mean, forward-fill, model-based) based on missingness mechanisms (MCAR, MAR, MNAR)
Validating engineered features for leakage using temporal holdout checks in time-series contexts
Enforcing referential integrity when joining features across distributed data sources
Tracking feature validity windows to prevent stale data usage in real-time systems
Implementing monotonicity constraints during encoding to preserve business logic
Versioning feature transformations to ensure reproducibility across training and inference
Validating distribution stability of derived features across batches and time windows
Documenting feature assumptions in a central feature catalog for cross-team reuse

Module 4: Data Validation in ML Pipelines

Embedding validation steps in TFX, Kubeflow, or custom Airflow DAGs to halt pipeline execution on critical failures
Defining per-feature validation rules (range, uniqueness, regex patterns) in schema files
Handling schema evolution during source system upgrades without breaking downstream models
Implementing soft vs. hard validation rules based on business criticality of data fields
Generating synthetic data to test validation logic under edge-case scenarios
Logging validation outcomes to a metadata store for root cause analysis during model degradation
Coordinating schema changes with model retraining schedules to minimize downtime
Using validation results to trigger data curation workflows or notify data stewards

Module 5: Monitoring Data Quality in Production ML Systems

Deploying continuous monitoring of input feature distributions using streaming frameworks (e.g., Apache Beam)
Correlating data quality alerts with model performance drops in monitoring dashboards
Setting up drift detection on prediction confidence scores as a proxy for data degradation
Integrating data quality signals into model retraining triggers and rollback decisions
Calculating and visualizing data freshness metrics for time-dependent features
Implementing shadow mode validation to compare new data batches against historical norms
Designing alert routing rules to direct data issues to the correct engineering or business teams
Using canary deployments to assess data quality impact on new model versions

Module 6: Governance and Compliance in ML Data Workflows

Mapping data lineage for audit purposes to meet regulatory requirements (e.g., GDPR, SR 11-7)
Implementing role-based access controls on sensitive features used in model training
Documenting data quality decisions in model risk management (MRM) artifacts for financial services
Conducting bias audits on input data using disaggregated quality metrics by demographic groups
Enforcing data retention policies in feature stores to comply with data minimization principles
Validating third-party data providers against contractual data quality SLAs
Creating data quality exception logs with approval workflows for temporary deviations
Standardizing metadata tagging to support regulatory reporting and model explainability

Module 7: Cross-System Data Integration Challenges

Resolving entity resolution conflicts when merging customer records from CRM and transaction systems
Handling time zone and clock skew issues in event data ingested from global sources
Aligning data dictionaries and business definitions across departments during data consolidation
Implementing idempotent ingestion logic to prevent duplication in retry scenarios
Validating referential integrity between parent-child relationships in denormalized datasets
Designing reconciliation jobs to detect and report discrepancies between source and target systems
Choosing between batch and streaming ingestion based on data freshness and quality trade-offs
Managing schema mismatches when integrating legacy systems with modern data platforms

Module 8: Scaling Data Quality Practices in Enterprise Environments

Centralizing data quality rules in a shared library to ensure consistency across ML teams
Implementing data quality scorecards to benchmark datasets used in multiple models
Automating data validation on newly registered datasets using metadata-driven pipelines
Integrating data quality metrics into model validation gates in MLOps platforms
Allocating compute resources for profiling and validation to avoid pipeline bottlenecks
Establishing data quality SLAs between data platform teams and ML consumers
Conducting root cause analysis using incident post-mortems to reduce recurring data issues
Training data engineers on ML-specific quality requirements beyond traditional BI use cases

Module 9: Incident Response and Remediation for Data Quality Failures

Defining escalation paths for data incidents based on business impact severity
Implementing automated rollback to last known-good dataset version during critical failures
Conducting blameless post-mortems to identify systemic gaps in data validation coverage
Creating runbooks for common data quality failure scenarios (e.g., upstream schema change)
Coordinating communication between data, ML, and business teams during data outages
Storing corrupted data samples for forensic analysis and test case development
Updating validation rules and monitoring thresholds based on incident learnings
Validating remediation steps in staging environments before re-enabling production pipelines