AI Infrastructure
Audience: Executive Leadership
Duration: 0.5 Day
Delivery: Virtual or in-person
Course description:
This module explains the infrastructure, data engineering, and model‑management foundations required to build reliable, secure, and scalable AI capabilities within the Government of Canada and defence environments.
Course overview:
GoC has a data engineering issue! — why federal departments struggle with fragmented data, legacy systems, inconsistent pipelines, and limited interoperability, and how these constraints directly limit AI readiness and operational advantage.
Model management — the lifecycle practices needed to deploy, monitor, secure, version, and retire AI models responsibly, including drift detection, auditability, and defence‑grade assurance.
Data as fuel for AI engines — how data ingestion, cleaning, transformation, and orchestration pipelines create the reliable, high‑quality data flows that AI systems depend on.
Let’s talk Microsoft / Google / AWS / Open Source — the strengths, limitations, and strategic considerations of major cloud and open‑source ecosystems, including sovereignty, security, interoperability, and alignment with GoC digital standards.
Visualization & reporting — how dashboards, reporting tools, and analytical visualizations translate complex data and model outputs into clear, actionable insight for commanders, executives, and operational teams.
By the end of this course, participants will be able to:
Explain the infrastructure and data‑engineering foundations required to build secure, scalable AI capabilities
Identify the key data‑engineering challenges facing federal departments and how they limit AI readiness and operational advantage
Understand the full lifecycle of model management, including deployment, monitoring, drift detection, versioning, and auditability
Describe how data ingestion, cleaning, transformation, and orchestration pipelines fuel reliable AI systems
Compare major cloud and open‑source ecosystems (Microsoft, Google, AWS, Open Source) and assess their alignment with GoC security, sovereignty, and interoperability requirements
Recognize how visualization and reporting tools translate complex model outputs into actionable insight for executives, commanders, and operational teams
Assess organizational gaps and opportunities related to infrastructure, governance, and data maturity that influence AI adoption