Description

This curriculum spans the technical and operational rigor of a multi-workshop program focused on production-grade implementation of emerging technologies, comparable to an internal capability buildout for managing complex, large-scale application environments.

Module 1: Strategic Adoption of Containerization Platforms

Evaluate the migration of monolithic applications to containerized architectures using Docker, considering stateful service dependencies and persistent storage requirements.
Design namespace and resource quota policies in Kubernetes to enforce fair sharing across development, staging, and production workloads.
Implement pod security policies or OPA Gatekeeper constraints to restrict privileged container execution in multi-tenant clusters.
Configure horizontal pod autoscalers based on custom metrics exported via Prometheus, balancing responsiveness with cost implications of over-provisioning.
Integrate image scanning into CI/CD pipelines to block deployment of containers with critical CVEs in base layers or dependencies.
Plan cluster upgrade strategies using node pools and rolling updates to minimize application downtime during Kubernetes version changes.

Module 2: Observability in Distributed Systems

Instrument applications with OpenTelemetry SDKs to generate correlated traces, logs, and metrics without introducing performance bottlenecks.
Configure log sampling strategies in high-throughput services to reduce ingestion costs while preserving debuggability for error conditions.
Define SLOs and error budgets using Prometheus and Grafana, then configure alerting policies that trigger only on user-impacting degradation.
Deploy service mesh sidecars (e.g., Istio) selectively based on security and observability requirements, avoiding overhead for internal batch workloads.
Design log retention policies that comply with regulatory requirements while managing storage costs across development and production environments.
Correlate backend trace data with frontend RUM (Real User Monitoring) events to identify performance issues in end-to-end user journeys.

Module 3: Infrastructure as Code Governance

Structure Terraform modules to support environment parity while allowing controlled divergence for region-specific configurations.
Implement policy-as-code checks using HashiCorp Sentinel or Open Policy Agent to prevent unapproved resource types or configurations.
Manage state file locking and backend configuration in Terraform to prevent concurrent execution conflicts in team environments.
Rotate cloud provider credentials used in CI/CD pipelines and IaC tools according to organizational security policies.
Enforce drift detection workflows to identify and reconcile manual changes made outside of IaC definitions.
Version and test infrastructure modules independently to enable safe reuse across multiple application teams.

Module 4: Serverless Architecture Design and Limits

Partition event-driven workloads across AWS Lambda or Azure Functions based on execution duration, memory needs, and concurrency limits.
Design retry and dead-letter queue strategies for asynchronous serverless functions to handle transient failures without data loss.
Optimize cold start performance by selecting appropriate runtime, memory allocation, and provisioned concurrency settings.
Implement observability in serverless functions by injecting correlation IDs and exporting structured logs to centralized systems.
Manage permissions for serverless functions using least-privilege IAM roles scoped to specific resources and actions.
Assess cost implications of high-frequency invocations and data transfer patterns when replacing traditional APIs with serverless endpoints.

Module 5: AI-Driven Operations and AIOps Integration

Deploy anomaly detection models on time-series metrics to reduce false positives in alerting compared to static thresholds.
Integrate natural language processing tools to parse incident tickets and suggest known resolutions from historical runbooks.
Evaluate the accuracy and maintenance burden of predictive scaling models trained on historical load patterns.
Use clustering algorithms to group related alerts and reduce incident noise during system-wide outages.
Validate model drift in production AIOps systems by monitoring prediction confidence and retraining schedules.
Balance automation scope in remediation workflows to avoid overreach that could escalate incidents during partial failures.

Module 6: Secure Software Supply Chain Management

Enforce signed commits and artifact provenance using Sigstore or in-toto to verify the origin of code changes in CI pipelines.
Implement SBOM (Software Bill of Materials) generation and vulnerability scanning at multiple stages of the build process.
Configure private artifact repositories with role-based access and retention policies to prevent unauthorized package exposure.
Adopt dependency pinning and regular update cycles to mitigate risks from transitive open-source dependencies.
Enforce build reproducibility by standardizing container build contexts and base image versions across environments.
Conduct supply chain risk assessments for third-party SaaS tools integrated into the deployment pipeline.

Module 7: Edge Computing and Latency-Sensitive Deployments

Distribute application components across regional edge locations based on user density and latency SLAs.
Implement conflict resolution strategies for offline-first edge applications using CRDTs or timestamp-based merging.
Manage firmware and software updates for edge nodes using staged rollouts and remote attestation.
Design data egress policies to minimize bandwidth costs when synchronizing edge data with central data lakes.
Enforce physical security and tamper detection mechanisms for unattended edge devices in remote locations.
Balance compute allocation between edge nodes and central cloud based on processing requirements and data sensitivity.

Module 8: Technology Lifecycle and Deprecation Planning

Establish deprecation timelines for legacy APIs based on usage metrics and downstream consumer readiness.
Communicate breaking changes through versioned documentation, changelogs, and direct notifications to integration teams.
Migrate data from retiring systems using dual-write patterns followed by validation and cutover.
Decommission infrastructure components only after confirming zero traffic and completing audit log retention.
Archive application configurations and deployment scripts for compliance and historical reference post-retirement.
Conduct post-mortems on decommissioned systems to capture technical debt and inform future architecture decisions.