Description

This curriculum spans the technical, operational, and organisational challenges of deploying and maintaining machine learning systems at scale, comparable in scope to a multi-phase internal capability program for establishing a centralised ML platform within a regulated enterprise.

Module 1: Platform Selection and Vendor Evaluation

Compare managed ML platforms (e.g., SageMaker, Vertex AI) against open-source stacks (e.g., MLflow, Kubeflow) based on team expertise and operational overhead tolerance.
Evaluate licensing costs and usage-based pricing models across cloud providers when scaling inference workloads.
Assess platform support for hybrid or on-prem deployment due to data residency requirements in regulated industries.
Negotiate SLAs with cloud providers for model hosting uptime and incident response timelines.
Validate platform compatibility with existing data warehouse and ETL tooling (e.g., Snowflake, Airflow).
Conduct proof-of-concept benchmarks for training job performance across GPU instance types and regions.
Document audit trail capabilities for compliance with internal security policies during vendor selection.
Integrate platform APIs into CI/CD pipelines to test deployment automation before vendor lock-in.

Module 2: Data Governance and Feature Management

Design a centralized feature store schema with consistent naming, versioning, and access controls across business units.
Implement data quality checks at ingestion to detect schema drift in streaming feature pipelines.
Enforce role-based access to sensitive features (e.g., PII-derived) using attribute-based access control (ABAC).
Establish data lineage tracking from raw sources to model inputs using metadata tagging.
Balance feature freshness against computational cost in real-time serving architectures.
Define retention policies for historical feature data based on model retraining cycles and compliance.
Coordinate with legal teams to document data provenance for GDPR and CCPA compliance.
Automate feature documentation updates using schema change detectors in the data pipeline.

Module 3: Model Development and Experiment Tracking

Standardize experiment logging formats across teams to enable cross-project model comparison.
Configure distributed training jobs with parameter servers or all-reduce strategies based on model size.
Implement early stopping and hyperparameter search budgets to control cloud compute spend.
Version model artifacts, code, and data splits using hash-based identifiers for reproducibility.
Enforce code review requirements for model training scripts before merging to main branch.
Isolate experimental dependencies using container images to prevent environment drift.
Set thresholds for metric improvements to qualify as production-ready model candidates.
Archive stale experiments to reduce clutter and optimize metadata storage costs.

Module 4: Model Deployment and Serving Infrastructure

Choose between batch, real-time, or streaming inference based on business SLA requirements.
Configure autoscaling policies for model endpoints using request rate and GPU utilization metrics.
Deploy shadow models to production to compare predictions against live systems without routing traffic.
Implement canary rollouts with traffic shifting increments of 5–10% to monitor performance.
Containerize models using ONNX or TorchScript to decouple from training frameworks.
Optimize model serialization format and payload size to reduce inference latency.
Enforce TLS encryption and mTLS authentication between client applications and model servers.
Design fallback mechanisms for model downtime using last-known-good predictions or rule-based logic.

Module 5: Monitoring and Observability

Instrument model endpoints with structured logging for request, response, and processing time.
Set up alerts for prediction drift using statistical tests (e.g., Kolmogorov-Smirnov) on output distributions.
Monitor feature drift by comparing incoming data distributions to training set baselines.
Track data pipeline delays that impact feature freshness in real-time models.
Correlate model performance degradation with upstream data source outages or schema changes.
Log model bias metrics (e.g., demographic parity) periodically for fairness monitoring.
Integrate model logs into centralized observability platforms (e.g., Datadog, Splunk).
Define escalation paths for alert triage between data scientists, ML engineers, and SREs.

Module 6: Model Lifecycle and Retraining Strategies

Establish retraining triggers based on performance decay, data drift, or business rule updates.
Schedule periodic full retraining versus incremental updates based on data volume and staleness.
Coordinate model retraining with feature store schema updates to prevent compatibility issues.
Validate new model versions against a holdout dataset before promotion to staging.
Archive deprecated models with metadata indicating retirement reason and successor.
Implement model registry workflows with approval gates for promotion to production.
Track model lineage to audit which training data and code version produced a given deployment.
Conduct cost-benefit analysis of automated retraining versus manual intervention cycles.

Module 7: Security, Compliance, and Access Control

Encrypt model artifacts at rest using customer-managed keys in cloud storage.
Conduct penetration testing on model APIs to identify injection or exfiltration risks.
Audit access logs to model endpoints and training environments for anomalous behavior.
Implement model watermarking or fingerprinting to detect unauthorized redistribution.
Classify models as critical assets and include them in enterprise risk assessments.
Enforce multi-factor authentication for access to model management consoles.
Restrict model download capabilities to prevent local execution outside controlled environments.
Document model data usage for regulatory reporting (e.g., model risk management in banking).

Module 8: Cost Management and Resource Optimization

Right-size GPU instances for training jobs using profiling tools to avoid underutilization.
Implement spot instance fallback logic for non-critical training workloads.
Monitor idle model endpoints and automate shutdown during off-peak hours.
Compare cost-per-inference across model compression techniques (e.g., quantization, pruning).
Allocate cloud spending by team or project using billing tags and quotas.
Optimize feature computation costs by caching frequently used transformations.
Evaluate trade-offs between model accuracy and inference latency in cost-sensitive applications.
Forecast ML infrastructure spend based on retraining frequency and data growth trends.

Module 9: Cross-Functional Collaboration and Change Management

Define SLAs for model delivery timelines with product and engineering stakeholders.
Establish joint incident response protocols for model outages involving multiple teams.
Document model assumptions and limitations for business users in non-technical language.
Conduct model review boards with legal, compliance, and risk officers before deployment.
Align model KPIs with business outcomes (e.g., conversion rate, churn reduction).
Facilitate handoff from data science to ML engineering using standardized model cards.
Manage stakeholder expectations when model performance plateaus despite additional investment.
Implement feedback loops from customer support to identify model failure modes in production.