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AI Master’s · Become a MLOps Architect

Master's Program in Cloud AI & MLOps Engineering

Operationalize intelligence. Scale machine learning to the enterprise. Design scalable machine learning infrastructure. Automate the AI lifecycle on modern cloud platforms.

Duration
10 Weeks
Delivery
Online
Typical salary (Canada)
$105K–$170K CAD
Coming soonNot open for enrollment yet. See the programs open now →
Download the curriculum PDF
Why it matters

Skills employers are hiring for now

  • Global demand for this AI specialization bridges the gap between prototypes and production.
  • Companies struggle to find professionals who can maintain complex models in large-scale environments.
  • A fully STEM-aligned curriculum focusing on distributed systems and infrastructure.
  • Immigrants benefit from highly transferable, universally demanded job-ready skills.
  • Real-world industry use cases managing massive data pipelines and model deployments.
What makes it different

Built for real enterprise work

1

Hands-on projects building robust CI/CD pipelines for models.

2

Real enterprise use cases using AWS, GCP, and Azure.

3

Portfolio-ready work emphasizing infrastructure-as-code.

4

Interview preparation for specialized systems engineering roles.

5

Cloud deployment mastery using industry-standard tools.

6

Governance, compliance, and automated regulatory auditing awareness.

Curriculum

10 modules, fundamentals to production

Tap a module to see what you learn in it.

Download the curriculum PDFOpens in a new tab
Module 01 of 10

Foundations of Cloud AI Systems

Day 1 — Cloud AI Landscape
  • Evolution of cloud computing for AI
  • Overview of AWS, Azure, and Google Cloud AI ecosystems
  • Cloud-native architecture for AI systems
  • Containerization fundamentals with Docker
  • Microservices architecture for machine learning
  • Case Study: Netflix cloud infrastructure
  • Case Study: Uber real-time ML infrastructure
Day 2 — Data Infrastructure for AI
  • Data pipelines for machine learning
  • Streaming data platforms
  • Data lake vs data warehouse
  • Feature engineering pipelines
  • Data versioning strategies
  • Case Study: Amazon recommendation system data pipelines
  • Case Study: Shopify customer analytics pipelines
Day 3 — Cloud Storage for ML
  • Object storage architecture
  • Distributed storage systems
  • Data governance in cloud environments
  • Data security for AI systems
  • Large scale dataset management
  • Case Study: Tesla autonomous driving datasets
  • Case Study: Spotify music recommendation data architecture
Your career after this program

What you'll become, and what it can do for your pay

You can become
MLOps EngineerAI Platform EngineerMLOps Architect
🍁 In Canada
$105K–$170K
CAD / year
🇺🇸 In the US
$140K–$200K
USD / year
The typical move
DevOps or cloud engineer$85K–$120K CAD
MLOps Engineer$105K–$170K CAD

About 34% higher at the middle of the range

How it moves your career
  • Run AI in production: deployments, monitoring, CI/CD for models and cost control.
  • The reliability skills that keep AI fast, cheap and up for millions of users.
  • Builds directly on DevOps and cloud experience, so the move is quick and the pay jump is real.
  • A path to platform lead and architect roles that own the company's AI infrastructure.
Why it matters now

Getting a model to work once is easy; keeping it reliable and affordable in production is the hard part, and companies pay well for people who can.

Estimates from 2025–26 job postings (base salary). Typical ranges, not a guarantee: pay depends on your experience, city, employer and the job market.

Career outcomes

Where graduates go

MLOps EngineerCloud AI ArchitectMachine Learning Infrastructure Engineer
Typical salary range
$105K–$170K CAD
Production capstone

Design a cloud-based MLOps pipeline

Architect a robust, end-to-end MLOps pipeline on a public cloud that automates data ingestion, model training, evaluation, and scalable serving.

Portfolio-grade project

Admission

Bachelor’s degree
Or equivalent professional experience.
Some programming
Basic Python or backend knowledge helps.
Open worldwide
International students and career changers welcome.

Questions

Is this program suitable for beginners?+

Some programming helps, but the curriculum takes committed professionals from the foundations to enterprise deployment, step by step.

Is it fully online?+

Yes. It is delivered 100% online with live, practitioner-led sessions, so you can join from anywhere.

Do I get career support?+

Yes. You build a portfolio-grade capstone, practise technical interviews and get guidance on the roles you are targeting.

What jobs can I get after graduating?+

Graduates move into roles such as MLOps Engineer, Cloud AI Architect, Machine Learning Infrastructure Engineer.

Ready to become a MLOps Architect?

This program is coming soon. Enrollment is not open yet.

See programs open now