MLOps on the Cloud

Package, deploy, monitor and retrain models with pipelines, containers and CI/CD you can reuse at work.

⏱ 9 weeks🎚 Advanced🧩 2 projects · Capstone🖥 Any device · our GPUs

Draft syllabus. Timeline and topics are indicative and being finalised with the teaching team — the shape is right, the week-by-week detail may shift.

🎯 Who it's for

Engineers with ML basics — or DevOps engineers crossing into AI — building the path to production.

✅ Prerequisites

Python
Containers and Git
Basics of one cloud provider
Data Engineering for ML or equivalent recommended

Topics covered

Model packaging and registries
Containerised serving and autoscaling
CI/CD for machine learning

Monitoring and drift detection
Automated retraining
Cost and reliability

Expected completion timeline

9 weeks part-time at 8–12 hours per week ≈ 2.1 months. Self-paced learners can go faster; the live cohort keeps this pace.

Wk 1–3

Package and serve

You produce: A containerised model service with CI.

Wk 4–6

Monitor and alert

You produce: Drift and performance monitoring with alerts.

Wk 7–9

Retrain loop and capstone

You produce: An automated retraining pipeline and a runbook.

🧭 Where this fits

Core of the MLOps Engineer path and the DevOps → MLOps cross-over. See the career & salary map for the full path, the expected pay by region, and the total cost.

💳 Delivery & fees

Available in all four delivery formats. Fees vary by format and are confirmed on your advisor call; instalments available. [Add the fee and the next cohort date here.]

Add this to your plan

Book a call and we'll place this course in a full path toward the role you want.

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