Description

This curriculum spans the technical and operational complexity of a multi-workshop program focused on building and governing AI-driven data platforms, comparable to the iterative development cycles seen in enterprise data mesh implementations or large-scale ML system integrations.

Module 1: Architecting Scalable Data Ingestion Pipelines

Designing idempotent data ingestion workflows to handle duplicate messages from IoT devices and transactional systems.
Selecting between batch and streaming ingestion based on SLA requirements and source system capabilities.
Implementing schema validation at ingestion points to prevent downstream pipeline corruption from malformed JSON or Avro records.
Configuring backpressure mechanisms in Kafka consumers to prevent overload during traffic spikes from real-time feeds.
Integrating change data capture (CDC) tools with legacy RDBMS to minimize performance impact on production databases.
Establishing retry policies with exponential backoff for failed ingestion attempts from third-party APIs.
Partitioning strategies for high-volume event streams to balance parallelism and storage efficiency in data lakes.
Encrypting sensitive payloads in transit and at rest during ingestion from regulated data sources.

Module 2: Unified Data Modeling for AI Workloads

Choosing between star schema and wide-column layouts based on query patterns in ML feature stores.
Implementing slowly changing dimensions (SCD Type 2) for customer attributes in longitudinal AI training datasets.
Defining primary keys in distributed environments where UUIDs must be coordinated across microservices.
Resolving schema drift in multi-source datasets by enforcing schema evolution policies in schema registries.
Denormalizing transactional data for low-latency inference serving while maintaining auditability.
Modeling time-series data with TTL policies to manage storage costs in real-time anomaly detection systems.
Mapping unstructured text fields into structured embeddings during ETL for downstream NLP pipelines.
Validating referential integrity across distributed datasets where foreign key constraints cannot be enforced natively.

Module 3: Distributed Compute Orchestration

Configuring Spark executors with optimal memory overhead settings to prevent out-of-memory errors on large shuffle operations.
Choosing between Kubernetes and YARN for cluster orchestration based on existing DevOps tooling and team expertise.
Scheduling GPU-intensive training jobs with node affinity rules to ensure access to specialized hardware.
Implementing dynamic resource allocation in Spark to scale executors based on active task backlog.
Isolating production inference workloads from development jobs using namespace and quota management in K8s.
Managing Python dependency conflicts across ML jobs using containerized runtime images with pinned versions.
Monitoring speculative execution in Spark to identify straggler tasks without introducing redundant computation.
Configuring checkpointing intervals for long-running streaming jobs to balance fault tolerance and storage overhead.

Module 4: Feature Engineering at Scale

Designing time-windowed aggregations for behavioral features while avoiding label leakage in training data.
Implementing feature freshness SLAs to ensure real-time models receive updates within 500ms.
Versioning feature transformations to enable reproducible training across model iterations.
Materializing feature vectors into low-latency stores like Redis or DynamoDB for online inference.
Handling missing values in high-cardinality categorical features using target encoding with smoothing.
Securing access to sensitive features (e.g., PII) through attribute-based access control in feature stores.
Automating drift detection on input feature distributions using statistical process control.
Optimizing feature computation costs by caching intermediate results in delta tables with time travel.

Module 5: Model Training and Lifecycle Management

Selecting distributed training frameworks (e.g., Horovod vs. PyTorch DDP) based on model architecture and cluster topology.
Implementing early stopping with validation loss monitoring to reduce unnecessary compute spend.
Tracking hyperparameters, metrics, and artifacts using MLflow with centralized storage and access controls.
Registering models in a model registry with approval workflows for production promotion.
Managing training data lineage to support audit requirements in regulated industries.
Containerizing training jobs with reproducible environments using Docker and Conda.
Scaling hyperparameter tuning jobs with Bayesian optimization across preemptible cloud instances.
Archiving stale models and associated artifacts to meet data retention policies.

Module 6: Real-Time Inference Infrastructure

Choosing between serverless inference endpoints and dedicated serving clusters based on request patterns.
Implementing request batching strategies to improve GPU utilization under variable load.
Configuring health checks and liveness probes for model servers in Kubernetes deployments.
Enforcing rate limiting and circuit breakers to protect inference APIs from cascading failures.
Instrumenting inference requests with tracing headers for end-to-end latency analysis.
Managing A/B testing traffic splits at the load balancer level for controlled model rollouts.
Encrypting model payloads in transit using mTLS between internal services.
Implementing fallback mechanisms for degraded service when primary models are unavailable.

Module 7: Data and Model Governance

Classifying datasets by sensitivity level and applying encryption and masking policies accordingly.
Implementing data retention schedules in data lakes to comply with GDPR and CCPA.
Logging model prediction requests for auditability while minimizing storage of personal data.
Enforcing model approval gates using RBAC in CI/CD pipelines before production deployment.
Documenting data provenance from source to model output for regulatory submissions.
Conducting bias assessments on model outputs across demographic segments using statistical tests.
Managing consent flags for data usage in training pipelines with opt-out propagation.
Establishing data stewards with ownership responsibilities for critical AI datasets.

Module 8: Monitoring and Observability

Setting up anomaly detection on prediction latency metrics to identify infrastructure bottlenecks.
Tracking feature drift using Kolmogorov-Smirnov tests on production input distributions.
Correlating model performance degradation with upstream data pipeline failures using log aggregation.
Implementing structured logging in training jobs to enable root cause analysis of failures.
Creating dashboards that link data quality metrics to model accuracy trends over time.
Alerting on silent failures in batch scoring pipelines where outputs are generated but incorrect.
Sampling and storing inference requests for retrospective model analysis and debugging.
Monitoring resource utilization of model servers to detect memory leaks in long-running processes.

Module 9: Security and Compliance in AI Systems

Conducting threat modeling for AI endpoints to identify injection and adversarial attack vectors.
Scanning container images for vulnerabilities before deploying model servers to production.
Implementing role-based access control for model endpoints based on least privilege principles.
Encrypting model artifacts at rest using customer-managed keys in cloud storage.
Validating input payloads to prevent prompt injection attacks in generative AI services.
Redacting PII from training logs and monitoring outputs using named entity recognition.
Conducting penetration testing on AI APIs to evaluate resistance to model extraction attacks.
Documenting data processing activities to support Data Protection Impact Assessments (DPIAs).