Description

This curriculum spans the technical and operational rigor of a multi-workshop cloud modernization program, addressing the same infrastructure automation, security, and governance challenges encountered in enterprise AI platform rollouts across hybrid teams of DevOps, MLOps, and platform engineers.

Module 1: Cloud Provider Selection and Multi-Cloud Strategy

Evaluate regional availability of machine learning accelerators when choosing cloud providers for AI workloads.
Assess egress cost differentials between AWS, Azure, and GCP for large-scale model data transfers.
Design cross-cloud identity federation using SAML and OIDC to maintain centralized access control.
Implement landing zone architectures that enforce consistent networking and security baselines across accounts.
Decide between single-cloud optimization and multi-cloud redundancy based on SLA requirements and vendor lock-in risk.
Standardize Terraform module registries to support consistent provisioning across multiple cloud environments.
Negotiate enterprise agreements that include committed use discounts for sustained GPU instances.
Map compliance requirements (e.g., HIPAA, GDPR) to provider-specific compliance attestations and data residency options.

Module 2: Infrastructure as Code (IaC) for AI Environments

Enforce IaC policy using Open Policy Agent (OPA) to prevent untagged resources in production AI projects.
Structure Terraform workspaces to isolate staging, training, and inference environments with shared networking.
Implement secrets management integration between HashiCorp Vault and Kubernetes for model training jobs.
Use Atlantis to automate Terraform plan and apply workflows within CI/CD pipelines.
Version control state files in remote backends with state locking to prevent race conditions during parallel deployments.
Design reusable modules for GPU-optimized VMs with NVMe scratch storage and high-bandwidth networking.
Automate drift detection and remediation for critical AI inference endpoints using scheduled IaC reconciliation.
Integrate IaC scanning tools like Checkov into pull request pipelines to enforce security baselines.

Module 3: Secure AI Pipeline Orchestration

Configure Kubernetes pod security policies to restrict container privileges in model training clusters.
Implement mTLS between pipeline components using service meshes like Istio or Linkerd.
Enforce data access controls in Kubeflow Pipelines using namespace-based RBAC and OIDC integration.
Audit pipeline execution logs in Splunk or Datadog to detect anomalous behavior in model retraining jobs.
Isolate sensitive data preprocessing steps in air-gapped namespaces with egress filtering.
Rotate service account keys automatically using cloud IAM tools and integrate with workload identity.
Validate container images using Sigstore or cosign in CI before promoting to staging environments.
Apply network policies to restrict inter-pod communication in multi-tenant AI clusters.

Module 4: Scalable Model Training Infrastructure

Configure spot instance fallback logic in distributed training jobs to maintain throughput during capacity shortages.
Optimize data loading pipelines using S3 Select or BigQuery BI Engine to reduce I/O bottlenecks.
Design checkpointing strategies that balance storage cost against restart recovery time for long-running jobs.
Implement autoscaling groups tied to GPU utilization metrics for training workloads on EC2 or GKE.
Select between data parallelism and model parallelism based on model size and available instance topology.
Use cluster autoscaler with node pool taints to reserve high-memory nodes for large embedding models.
Integrate Horovod with cloud-native job schedulers to coordinate multi-node training efficiently.
Monitor training job convergence using TensorBoard hosted on secured cloud endpoints with SSO.

Module 5: Continuous Delivery for Machine Learning (CD4ML)

Define model promotion gates using statistical performance thresholds and data drift detection.
Integrate model versioning with MLflow or Vertex AI to track lineage from training to deployment.
Automate A/B test configuration in API gateways when promoting new model versions to production.
Implement canary rollouts for model endpoints with automated rollback based on error rate thresholds.
Store model artifacts in versioned cloud storage buckets with lifecycle policies to manage cost.
Enforce CI/CD pipeline stages that require security scanning and model explainability reports before deployment.
Use feature stores like Feast to synchronize training and serving feature transformations.
Orchestrate retraining pipelines using Airflow with dependency resolution across data and model stages.

Module 6: Monitoring and Observability in Production AI Systems

Instrument model inference endpoints with Prometheus to capture latency, throughput, and error rates.
Deploy distributed tracing across preprocessing, inference, and postprocessing services using OpenTelemetry.
Set up data drift alerts using statistical tests (e.g., Kolmogorov-Smirnov) on input feature distributions.
Correlate model performance degradation with upstream data pipeline failures using log context propagation.
Configure synthetic transactions to validate end-to-end model correctness during maintenance windows.
Aggregate model prediction logs in a centralized data lake for audit and regulatory reporting.
Implement circuit breakers in inference APIs to prevent cascading failures during model overload.
Use structured logging to capture model version, input features, and confidence scores for debugging.

Module 7: Cost Management and Resource Optimization

Apply reserved instance planning tools to forecast GPU usage and optimize long-term spend.
Implement automated shutdown policies for non-production Jupyter environments based on inactivity.
Right-size training clusters using historical job profiling data from cloud monitoring tools.
Negotiate custom machine types on GCP to match model memory and compute requirements precisely.
Use spot instance bidding strategies with fallback to on-demand for critical training deadlines.
Tag all AI resources with cost center, project, and owner metadata for chargeback reporting.
Monitor storage growth in model artifact repositories and apply lifecycle rules to delete stale versions.
Compare training cost per epoch across instance families to guide future infrastructure choices.

Module 8: Governance and Compliance for AI Workloads

Implement data retention policies in cloud storage to comply with model data provenance regulations.
Conduct model impact assessments to document bias testing and mitigation strategies for high-risk AI.
Enforce encryption at rest and in transit for all model artifacts and training datasets.
Integrate model registry with audit trails that log access, modification, and deployment events.
Design access review workflows for model deployment permissions using IAM certification campaigns.
Classify AI workloads by risk tier and apply differentiated security controls accordingly.
Document model lineage from data sourcing through training and deployment for regulatory audits.
Use data loss prevention (DLP) tools to scan model outputs for PII before external exposure.

Module 9: Disaster Recovery and High Availability for AI Services

Replicate model artifacts across regions using cross-region bucket replication with versioning.
Design active-passive inference clusters with automated failover using global load balancers.
Test backup restoration of Kubernetes cluster state and persistent volumes quarterly.
Pre-warm GPU instances in secondary regions to reduce recovery time during failover events.
Store training checkpoints in durable, multi-region storage to enable resume-after-failure.
Implement health checks that validate model availability and accuracy before routing traffic.
Document RTO and RPO targets for AI services and align infrastructure design to meet them.
Simulate region outages to validate DNS failover and data consistency across inference endpoints.