Machine Learning Engineering
Ship production-grade ML systems end-to-end
About this program
An intensive six-month program for software engineers and data scientists who need to move beyond notebooks. You'll architect, train, deploy, and monitor ML systems that survive contact with real users and real data. Heavy emphasis on Python tooling (PyTorch, scikit-learn), reproducible pipelines, feature stores, model registries, and the operational realities of running ML in production. Includes a Canadian case study on building ML systems that comply with provincial privacy law.
Student ratings
Exceptional — 266 verified Canadian graduates rated this program 4.8/5. Reviews emphasize the applied capstone, instructor responsiveness, and career outcomes.
- 5★226
- 4★33
- 3★5
- 2★1
- 1★1
Who this program is for
- →Practitioners already shipping ml engineering work who want depth
- →Senior engineers, data scientists, and technical leads
- →Canadian residents seeking a verifiable diploma credential
Topics you'll cover
6 modules across 6 months — 24 lessons in total.
Full syllabus
Module 1 · Month 1 — Engineering Foundations▾
- L1Reproducibility: seeds, environments, lockfiles
- L2Data versioning with DVC
- L3Project structure and code quality
- L4CI for ML codebases
Module 2 · Month 2 — Feature Engineering at Scale▾
- L1Feature stores (Feast)
- L2Online vs. offline features
- L3Backfills and point-in-time correctness
- L4Lab: building a feature pipeline
Module 3 · Month 3 — Training Pipelines▾
- L1From notebook to pipeline
- L2Hyperparameter search at scale
- L3Distributed training basics
- L4MLflow tracking and registries
Module 4 · Month 4 — Serving Models▾
- L1FastAPI and Triton inference server
- L2Batch vs. real-time inference
- L3GPU economics
- L4Canary and shadow deployments
Module 5 · Month 5 — Monitoring & Drift▾
- L1Data drift vs. concept drift
- L2Evidently and WhyLabs
- L3Building alerting that doesn't lie
- L4Root-cause investigation playbook
Module 6 · Month 6 — Capstone▾
- L1Design doc for a full ML system
- L2Implementation sprint
- L3Production deployment
- L4Postmortem and review
What you'll be able to do
- ●Architect production ML systems with clear data, training, and serving boundaries
- ●Build reproducible training pipelines with MLflow and DVC
- ●Deploy models behind low-latency REST and gRPC APIs
- ●Implement drift detection and shadow deployment
- ●Lead an ML team's engineering practices
Career paths after graduation
Frequently asked questions
How much does the Machine Learning Engineering cost?▾
Tuition is $175 CAD, paid once. You can pay in full at checkout or choose an interest-free monthly plan. A 30-day refund window applies from your enrollment date.
How long is the Machine Learning Engineering program?▾
Self-paced. Most students complete it in 6 months at roughly 10 hours per week, but you can go faster or slower — you keep lifetime access.
What are the prerequisites?▾
Strong Python; Working knowledge of SQL; Comfort with the command line
Is the diploma recognized in Canada?▾
Yes. Graduates receive the Altaris AI Academy Diploma in ML Engineering — a verifiable credential with a unique certificate number you can publish on LinkedIn and that any employer can verify at altarisai.org/verify.
What is the refund policy?▾
Full refund within 30 days of enrollment, no questions asked. After day 30, prorated refunds are available per our Refund Policy.
Who teaches the program?▾
Working Canadian AI practitioners — not academics. Every module is built and reviewed by a lead instructor working in the field today.
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