Description

This curriculum spans the full lifecycle of data management in production ML systems, equivalent in scope to a multi-workshop program embedded within an enterprise data science transformation, addressing technical, governance, and collaboration challenges encountered when scaling ML across business units.

Module 1: Defining Data Requirements for Business-Centric ML Use Cases

Align data scope with key performance indicators (KPIs) tied to business outcomes, such as customer retention or operational cost reduction.
Negotiate data access rights with legal and compliance teams when sourcing customer interaction logs from legacy CRM systems.
Determine whether real-time or batch data collection better supports the latency requirements of a fraud detection model.
Select data sources based on historical coverage depth, such as five years of transaction records to capture seasonal trends.
Document data lineage expectations early to ensure traceability from raw ingestion to model inference.
Balance data richness against collection cost when deciding whether to license third-party market data for demand forecasting.
Establish data ownership roles across business units to prevent duplication and inconsistent definitions.

Module 2: Designing Scalable and Secure Data Ingestion Pipelines

Choose between change data capture (CDC) and scheduled ETL jobs based on source system capabilities and data freshness needs.
Implement schema validation at ingestion to reject malformed records from IoT sensors before they pollute downstream systems.
Configure retry logic and dead-letter queues in streaming pipelines to handle transient API failures from external vendors.
Encrypt sensitive fields (e.g., PII) during transit and at rest using customer-managed keys in cloud storage.
Size compute resources for peak load during month-end financial data ingestion without over-provisioning.
Monitor ingestion latency to detect upstream system degradation before it impacts model training schedules.
Integrate ingestion logs with centralized observability platforms for audit and troubleshooting.

Module 3: Data Quality Assessment and Remediation Strategies

Define data quality rules per field, such as rejecting customer age values outside 18–120 for a lending model.
Quantify missing data patterns across time series to determine if imputation introduces bias in forecasting.
Use statistical profiling to detect silent data drift, such as a sudden drop in GPS signal frequency from delivery vehicles.
Escalate data quality issues to source system owners with evidence, such as duplicate order IDs in transaction feeds.
Implement automated data validation checks in CI/CD pipelines before promoting datasets to production.
Decide whether to exclude or correct outlier records in supply chain data based on operational context.
Track data quality metrics over time to measure improvement after upstream system fixes.

Module 4: Feature Engineering and Management at Scale

Version feature definitions in a shared repository to ensure consistency between training and serving environments.
Optimize feature computation latency by pre-aggregating daily sales totals instead of recalculating at inference.
Apply log transformations to skewed financial variables before feeding them into regression models.
Cache frequently used features in a feature store to reduce redundant computation across multiple models.
Enforce access controls on sensitive features, such as customer credit scores, using role-based policies.
Monitor feature drift by comparing statistical distributions in production versus training data.
Document business logic behind derived features, such as "customer engagement score," for audit and compliance.

Module 5: Building and Governing a Centralized Feature Store

Select between online and offline feature stores based on model latency requirements (e.g., real-time recommendations).
Define SLAs for feature availability and freshness, such as 99.9% uptime for features used in credit scoring.
Implement feature access approval workflows for regulated use cases involving health or financial data.
Archive unused features to reduce storage costs and simplify discovery for data scientists.
Integrate feature lineage tracking to support regulatory audits and root cause analysis.
Standardize feature naming conventions across teams to prevent duplication and confusion.
Monitor feature store query patterns to identify performance bottlenecks and optimize indexing.

Module 6: Managing Data Versioning and Reproducibility

Use content-addressed storage to version large datasets and ensure exact replication of training environments.
Link dataset versions to specific model training runs in a metadata registry for auditability.
Resolve conflicts when multiple teams modify the same dataset by implementing branching and merge strategies.
Automate data snapshotting before major model retraining cycles to enable rollback if needed.
Store data preprocessing code in version control alongside raw data references to ensure reproducible pipelines.
Archive outdated dataset versions according to data retention policies while preserving access for compliance.
Track dependencies between data versions and model versions to assess impact of data changes.

Module 7: Implementing Data Governance and Compliance Controls

Classify data assets by sensitivity level (e.g., public, internal, confidential) to enforce access policies.
Implement data masking for PII fields in non-production environments used for model development.
Conduct data protection impact assessments (DPIAs) for models processing personal health information.
Log all data access requests and approvals for regulatory reporting under GDPR or CCPA.
Establish data retention schedules for training datasets to comply with legal hold requirements.
Coordinate with legal teams to document data provenance for third-party licensed datasets.
Enforce data minimization by removing irrelevant fields before model training to reduce compliance risk.

Module 8: Monitoring Data Pipelines and Model Data Dependencies

Set up alerts for pipeline failures, such as a missing daily file from an external supplier.
Measure and track data freshness to ensure features are updated within required time windows.
Correlate data anomalies with model performance degradation using shared timestamps and logs.
Deploy shadow pipelines to validate new data transformations before switching live traffic.
Monitor schema evolution in source systems to detect breaking changes that affect feature computation.
Use data contracts to specify expected formats and constraints between data producers and consumers.
Integrate data health metrics into model monitoring dashboards for holistic observability.

Module 9: Enabling Cross-Functional Collaboration and Data Democratization

Develop a searchable data catalog with business-friendly metadata to improve discoverability.
Train business analysts to use self-service tools for accessing approved datasets without SQL.
Facilitate data review sessions between data scientists and domain experts to validate assumptions.
Implement data access request workflows that balance speed with security and compliance.
Standardize data dictionaries across departments to align on definitions like "active customer."
Host data quality retrospectives to share lessons from pipeline failures or model inaccuracies.
Measure adoption of shared data assets to justify continued investment in data infrastructure.