Description

This curriculum spans the technical, operational, and governance dimensions of deploying AI in big data environments, comparable in scope to a multi-phase internal capability program that integrates data engineering, MLOps, and enterprise risk management practices across departments.

Module 1: Defining Strategic AI Objectives in Big Data Contexts

Selecting use cases that align with enterprise data maturity and infrastructure capabilities, avoiding overreach into unstructured data without proper pipelines.
Assessing ROI of AI initiatives by quantifying data acquisition, labeling, and compute costs against projected operational efficiencies.
Deciding whether to prioritize predictive accuracy or model interpretability based on regulatory and stakeholder requirements.
Negotiating data access rights across business units to ensure sufficient training data without violating internal data governance policies.
Determining the scope of pilot projects to include only data sources with reliable lineage and metadata documentation.
Establishing KPIs for AI success that reflect business outcomes (e.g., reduced latency in fraud detection) rather than model metrics alone.
Integrating AI strategy with existing enterprise data architecture roadmaps to prevent siloed development.
Identifying executive sponsors whose business units will directly benefit from AI outcomes to ensure long-term support.

Module 2: Data Infrastructure for AI Workloads

Choosing between batch and streaming ingestion pipelines based on AI model refresh requirements and data velocity.
Designing data lake schemas (e.g., Medallion Architecture) to support versioned training datasets and reproducible experiments.
Implementing data partitioning strategies in distributed storage (e.g., Parquet on S3) to optimize query performance for feature extraction.
Configuring compute clusters (e.g., Spark on Kubernetes) with appropriate memory and CPU/GPU ratios for large-scale feature engineering.
Setting up data retention policies that balance cost with the need to retrain models on historical data.
Integrating metadata management tools (e.g., Apache Atlas) to track data lineage from source to model input.
Allocating dedicated staging environments for data scientists to prototype transformations without impacting production ETL.
Implementing data masking in non-production environments to comply with PII handling policies.

Module 3: Feature Engineering at Scale

Automating feature computation using windowed aggregations over streaming data for real-time model inputs.
Managing feature store consistency across training and serving environments to prevent training-serving skew.
Selecting between online and offline feature stores based on latency requirements and update frequency.
Versioning feature sets to enable model reproducibility and A/B testing of different feature combinations.
Handling missing data in high-cardinality categorical features using statistical imputation strategies that scale.
Applying dimensionality reduction techniques (e.g., PCA) only after validating that they preserve signal for the target variable.
Monitoring feature drift by comparing statistical distributions in production data against training baselines.
Documenting feature definitions and business logic in a centralized catalog accessible to data engineers and scientists.

Module 4: Model Development and Validation

Selecting model families (e.g., gradient-boosted trees vs. deep learning) based on data size, sparsity, and interpretability needs.
Implementing cross-validation strategies that account for temporal dependencies in time-series data.
Validating model performance on stratified holdout sets that reflect real-world class imbalances.
Conducting bias audits using disaggregated evaluation metrics across demographic or operational segments.
Using synthetic data generation only when real data is legally restricted, and validating that synthetic distributions match reality.
Setting up automated testing pipelines to catch model performance regressions during retraining.
Defining model calibration requirements for probabilistic outputs used in decision thresholds.
Documenting model assumptions and limitations in a model card for stakeholder review.

Module 5: Model Deployment and Serving

Choosing between synchronous and asynchronous inference APIs based on application latency SLAs.
Containerizing models using Docker and orchestrating with Kubernetes for scalable, monitored deployments.
Implementing canary rollouts to gradually shift traffic to new model versions and monitor for anomalies.
Integrating model serving with feature stores to ensure consistent input data at inference time.
Configuring auto-scaling policies for inference endpoints to handle variable load without over-provisioning.
Deploying models on edge devices only when network latency or data privacy requirements justify the operational complexity.
Setting up model version rollback procedures triggered by performance degradation alerts.
Encrypting model artifacts in transit and at rest when deployed in multi-tenant environments.

Module 6: Monitoring and Maintenance in Production

Tracking prediction latency, error rates, and throughput to detect infrastructure or model degradation.
Implementing data drift detection using statistical tests (e.g., Kolmogorov-Smirnov) on input feature distributions.
Setting up automated retraining triggers based on performance decay or data staleness thresholds.
Logging model inputs and outputs for auditability, while applying data minimization to avoid storing PII.
Creating dashboards that correlate model performance with business metrics (e.g., conversion rates, false positives).
Establishing incident response protocols for model failures, including fallback mechanisms and stakeholder notifications.
Rotating model keys and access credentials on a scheduled basis to maintain security compliance.
Conducting quarterly model reviews to assess continued relevance and performance in changing business conditions.

Module 7: Data and Model Governance

Applying role-based access control (RBAC) to model development environments and production endpoints.
Registering models in a central model registry with metadata on version, owner, training data, and evaluation results.
Conducting DPIAs (Data Protection Impact Assessments) for models processing personal data under GDPR or similar regulations.
Implementing model explainability reports for high-risk decisions (e.g., credit scoring, hiring) to meet regulatory requirements.
Enforcing model validation gates before deployment using CI/CD pipelines integrated with testing frameworks.
Archiving deprecated models and associated data to meet retention policies without disrupting active systems.
Requiring documentation of model training data sources to support reproducibility and audit requests.
Establishing a model review board to approve high-impact models before production release.

Module 8: Scaling AI Across the Enterprise

Standardizing on a common AI platform stack to reduce duplication and support centralized monitoring.
Creating reusable feature templates and model blueprints to accelerate development across teams.
Allocating shared GPU resources with quotas to balance cost and access across departments.
Developing internal training programs to upskill data engineers on MLOps tools and practices.
Integrating AI outputs into existing business intelligence and reporting systems for broader consumption.
Establishing a center of excellence to govern best practices, tool selection, and knowledge sharing.
Measuring adoption through usage metrics of deployed models and feature store access patterns.
Conducting post-mortems on failed AI initiatives to refine selection criteria and risk assessment.

Module 9: Ethical and Operational Risk Management

Implementing fairness constraints during model training when historical data reflects systemic biases.
Conducting red team exercises to test models for adversarial inputs or manipulation.
Assessing the environmental impact of large-scale model training and optimizing for energy efficiency.
Limiting model autonomy in critical systems by requiring human-in-the-loop approvals for high-stakes decisions.
Documenting known failure modes and edge cases in model risk assessments for internal audit.
Designing fallback logic (e.g., rule-based systems) to maintain operations during model downtime.
Requiring third-party model vendors to provide transparency on training data and performance benchmarks.
Updating incident response plans to include AI-specific scenarios such as model poisoning or data leakage.