Description

This curriculum spans the technical and operational complexity of a multi-workshop program focused on enterprise ML infrastructure, covering the design, deployment, and governance of data pipelines and models at scale, comparable to an internal capability-building initiative for data platform teams in large organisations.

Module 1: Strategic Alignment of Big Data Infrastructure with Machine Learning Objectives

Selecting data storage architectures (data lake vs. data warehouse) based on model retraining frequency and feature engineering complexity.
Defining data retention policies that balance compliance requirements with the need for longitudinal training datasets.
Mapping business KPIs to model performance metrics during the initial scoping phase to ensure alignment with operational outcomes.
Deciding between batch and real-time data ingestion based on use case SLAs and infrastructure cost constraints.
Establishing cross-functional steering committees to prioritize data pipeline investments against business unit demands.
Integrating model lifecycle stages into enterprise data governance frameworks to enforce consistency across teams.
Evaluating cloud provider data egress costs when designing distributed training workflows across regions.
Allocating shared data resources across competing ML initiatives using capacity planning models.

Module 2: Data Acquisition, Ingestion, and Pipeline Orchestration

Configuring idempotent data ingestion jobs to prevent duplication in distributed streaming environments.
Implementing schema validation and versioning in Kafka or Kinesis pipelines to maintain compatibility across model versions.
Designing retry and dead-letter queue strategies for failed records in high-throughput ETL systems.
Selecting between Change Data Capture (CDC) and API polling based on source system capabilities and latency requirements.
Partitioning large datasets by time and entity to optimize query performance in distributed file systems like Parquet on S3.
Orchestrating complex DAGs in Airflow or Prefect with conditional branching based on data quality thresholds.
Monitoring pipeline latency and backpressure in real-time streams to trigger model retraining alerts.
Securing data in transit using mutual TLS and in-flight encryption for sensitive customer data ingestion.

Module 3: Feature Engineering at Scale

Building reusable feature stores with metadata tracking to enable cross-team feature discovery and reuse.
Implementing point-in-time correct feature lookups to prevent data leakage during model training.
Choosing between online and offline feature stores based on model serving latency requirements.
Automating feature drift detection using statistical tests on daily feature distributions.
Managing feature lineage from raw data to model input to support audit and debugging workflows.
Optimizing feature computation using vectorized operations in Spark or Dask for large-scale transformations.
Designing feature encoding strategies for high-cardinality categorical variables with production memory constraints.
Versioning feature sets to align with model versioning for reproducible training runs.

Module 4: Distributed Model Training and Hyperparameter Optimization

Partitioning training data across GPU nodes using distributed data parallelism in PyTorch or TensorFlow.
Configuring spot instances for cost-effective hyperparameter sweeps with checkpointing and resume logic.
Selecting between synchronous and asynchronous parameter updates based on cluster stability and convergence needs.
Implementing early stopping rules tied to validation loss plateaus in automated training pipelines.
Managing shared model registry access to prevent race conditions during distributed training jobs.
Designing custom loss functions to incorporate business costs into model optimization objectives.
Scaling embedding layers for large vocabularies using sharding strategies in recommendation systems.
Monitoring GPU utilization and memory allocation to detect bottlenecks in distributed training clusters.

Module 5: Model Evaluation and Validation in Production Contexts

Designing holdout datasets that reflect future data distributions using time-based splits.
Implementing A/B test frameworks to isolate model impact from external market variables.
Calculating fairness metrics across demographic segments to identify unintended model bias.
Validating model performance under data scarcity conditions using bootstrapped confidence intervals.
Establishing performance baselines using business rules or legacy models for comparison.
Monitoring prediction consistency across model versions to detect silent regressions.
Conducting root cause analysis on model decay using feature attribution methods like SHAP.
Defining escalation protocols for performance degradation beyond predefined thresholds.

Module 6: Model Deployment, Serving, and Scalability

Selecting between REST, gRPC, or message queues for model serving based on latency and throughput needs.
Implementing blue-green deployments for models to minimize downtime during updates.
Configuring autoscaling policies for inference endpoints based on request rate and latency metrics.
Using model quantization to reduce serving latency and memory footprint on edge devices.
Integrating circuit breakers and rate limiting to protect downstream services from model overload.
Containerizing models with Docker and managing dependencies to ensure environment consistency.
Deploying ensemble models with weighted voting strategies across multiple inference endpoints.
Implementing batch inference for high-volume, low-latency scenarios using asynchronous processing.

Module 7: Monitoring, Logging, and Feedback Loops

Instrumenting model endpoints with structured logging to capture input, output, and metadata for audit.
Tracking prediction drift using Kolmogorov-Smirnov tests on model output distributions.
Correlating model performance degradation with upstream data pipeline incidents using log aggregation.
Designing feedback loops to capture user actions post-prediction for implicit label generation.
Setting up anomaly detection on inference request patterns to identify potential abuse or failures.
Storing prediction logs in a queryable format to support ad hoc model debugging and analysis.
Calculating and monitoring feature importance stability over time to detect concept drift.
Integrating model monitoring alerts into existing IT operations dashboards and ticketing systems.

Module 8: Data and Model Governance

Classifying data sensitivity levels to enforce differential access controls in model development environments.
Documenting model decisions in model cards to support regulatory compliance and internal audits.
Implementing role-based access control (RBAC) for model registry and feature store operations.
Conducting DPIAs (Data Protection Impact Assessments) for models processing personal data.
Enforcing model signing and checksum validation to prevent unauthorized model deployment.
Archiving training data snapshots and model artifacts for reproducibility and legal discovery.
Establishing data lineage tracking from source systems to model predictions for transparency.
Coordinating model risk assessments with legal and compliance teams for high-impact use cases.

Module 9: Cost Management and Resource Optimization

Right-sizing GPU instances for training jobs based on memory and compute benchmarks.
Implementing model pruning and distillation to reduce inference infrastructure costs.
Tracking per-model cloud spend using cost allocation tags and chargeback models.
Optimizing data serialization formats (e.g., Avro, Parquet) to reduce storage and transfer costs.
Scheduling non-critical training jobs during off-peak hours to leverage lower compute rates.
Implementing caching layers for expensive feature computations to reduce redundant processing.
Conducting TCO analysis for on-premise vs. cloud-based inference infrastructure.
Automating shutdown of development environments to eliminate idle resource consumption.