Description

This curriculum spans the breadth of a multi-workshop program on machine learning operations, addressing the technical, governance, and lifecycle management challenges teams encounter when deploying and maintaining feature selection practices in production business systems.

Module 1: Defining Business Objectives and Aligning Feature Selection Goals

Selecting target variables based on measurable business KPIs such as customer churn rate or average order value, rather than model accuracy alone.
Mapping predictive modeling goals to operational decisions, such as determining whether a model will support real-time scoring or batch risk assessment.
Identifying data latency constraints by evaluating whether near-real-time features (e.g., last login time) are feasible given source system update cycles.
Deciding between global versus segmented models based on business unit requirements, which affects feature relevance across customer cohorts.
Establishing thresholds for model interpretability when compliance or stakeholder communication requires clear feature impact explanations.
Documenting feature lifecycle ownership to clarify accountability for updates when business logic changes (e.g., new product categories).

Module 2: Data Inventory and Feature Readiness Assessment

Conducting a lineage audit to trace each candidate feature from source system to warehouse, identifying transformation points that may introduce bias.
Assessing feature availability at prediction time by verifying whether variables like credit score are accessible during live inference.
Quantifying missing data patterns across features to determine imputation feasibility or exclusion (e.g., 60% missing income data in CRM).
Evaluating feature update frequency against model refresh cycles to avoid stale inputs in production (e.g., monthly billing data in a daily model).
Flagging features derived from manual entry or third-party APIs that introduce operational fragility and monitoring overhead.
Classifying features by data type and scale to inform preprocessing requirements (e.g., log-transforming monetary amounts).

Module 3: Statistical and Correlation-Based Filtering

Applying variance thresholds to remove near-constant features (e.g., 99.5% zero values in promotional flag fields).
Using pairwise correlation matrices to detect redundant features, such as multiple tenure measures from overlapping date fields.
Implementing ANOVA F-tests to evaluate the significance of categorical features against continuous targets in regression tasks.
Excluding features with high correlation to the target in time-series settings to prevent look-ahead bias (e.g., next-month balance used as input).
Adjusting p-value thresholds based on multiple testing corrections when screening thousands of features simultaneously.
Retaining theoretically important features despite low statistical scores when domain knowledge suggests delayed or nonlinear effects.

Module 4: Model-Driven Feature Importance and Wrapper Methods

Running recursive feature elimination with cross-validation to identify minimal feature sets that maintain performance on validation folds.
Comparing permutation importance across models (e.g., Random Forest vs. XGBoost) to assess stability of feature rankings.
Configuring early stopping in iterative selection to avoid overfitting during nested cross-validation loops.
Monitoring computational cost when applying wrapper methods to high-cardinality datasets, limiting candidate features to 500 for feasibility.
Interpreting SHAP values to detect interactions (e.g., age and income) that justify retaining both features despite moderate individual importance.
Documenting the performance delta between full and reduced models to justify selection decisions to technical stakeholders.

Module 5: Handling Multicollinearity and Feature Engineering Trade-offs

Calculating variance inflation factors (VIF) to identify and remove collinear features in linear models affecting coefficient stability.
Deciding whether to keep original features or principal components based on stakeholder interpretability requirements.
Managing the risk of over-engineering by capping the number of derived features per source variable (e.g., no more than three lag features).
Validating engineered features against business logic, such as ensuring rolling averages align with operational reporting periods.
Tracking feature creation dates and dependencies to support debugging when model performance degrades.
Using domain-specific transformations (e.g., RFM scoring) only when historical analysis confirms predictive lift.

Module 6: Operational Constraints and Production Readiness

Enforcing feature schema validation in the inference pipeline to prevent model errors from unexpected nulls or type changes.
Designing fallback logic for missing features during scoring, such as using cohort averages when individual values are unavailable.
Implementing feature versioning to support A/B testing and rollback capabilities in production environments.
Measuring feature computation latency to ensure real-time models meet SLAs (e.g., sub-100ms feature extraction).
Registering features in a central catalog with metadata including ownership, update frequency, and business definition.
Setting up monitoring for feature distribution drift using statistical tests (e.g., Kolmogorov-Smirnov) on weekly batches.

Module 7: Governance, Compliance, and Ethical Considerations

Screening features for protected attributes or proxies (e.g., ZIP code as a race surrogate) to comply with fair lending regulations.
Documenting data provenance for audit purposes, especially when features originate from third-party vendors.
Implementing role-based access controls on feature stores to restrict sensitive data (e.g., health indicators) to authorized teams.
Conducting bias audits by stratifying model performance across demographic groups defined by available features.
Archiving deprecated features with retention policies aligned to legal and compliance requirements.
Requiring change control approvals for modifications to high-impact features used in regulated decisioning systems.

Module 8: Iterative Refinement and Monitoring in Live Systems

Scheduling quarterly re-evaluation of feature importance to detect decay due to market or behavioral shifts.
Integrating feature performance metrics (e.g., AUC drop when removed) into model monitoring dashboards.
Triggering retraining pipelines when feature availability drops below 95% in production data streams.
Using shadow mode deployments to test new features without impacting live decisions or customer experiences.
Logging feature values alongside predictions to enable post-hoc analysis of model behavior in edge cases.
Establishing feedback loops from business units to report when model outputs contradict observed trends, prompting feature review.