Cloud Infrastructure for ML Teams
Covers the fundamentals most practitioners skip — IAM, networking, storage tiers, and cost controls. Designed for those moving from local notebooks to shared cloud environments for the first time.
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Upcoming programmes · 2025
Cloud platforms
Cloud platforms
for AI work
These workshops are built around real cloud environments — not slides. Each session puts you inside actual infrastructure decisions: choosing compute, wiring data pipelines, deploying models. The gap between knowing and doing closes faster when the environment is live.
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Cohorts opening
this season
Seats are capped so that instructors can give real feedback on your work — not just watch a progress bar fill. Each cohort runs over eight weeks with structured checkpoints.
Managed AI Services Across AWS, GCP & Azure
A comparative, hands-on track. You work through the same task on three platforms — SageMaker, Vertex AI, Azure ML — and leave with a clear picture of where each makes sense and where it does not.
View detailsDeployment Pipelines & Model Serving at Scale
Focuses on the production side — CI/CD for ML, container orchestration, latency tuning, and monitoring in live environments. Prerequisites: comfort with Docker and at least one managed ML service.
View detailsWhat the format actually involves
Each week combines a guided live session with independent lab work on a shared cloud environment we provision for you. Instructors review submitted work and leave written notes — not just scores. The pace is demanding but realistic: roughly six to eight hours per week outside live sessions. Participants from across Canada join the same cohort, and the time zone spread is managed through recorded sessions and async discussion threads.
Programme at a glance
3
Cohorts opening this season
18
Max seats per cohort
6–8
Hours per week expected