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Design, Cost-Model & Ship Machine-Learning Systems Like a Staff Engineer

12 Weeks. Live Online Classes. Instructor-led.

Our Partners

Master blue-green & canary roll-outs, observability, and failure playbooks

Draft production design docs that balance accuracy, latency & cost

Build feature stores, offline/online parity and reliability budgets

What you will learn?

Think like a Staff Engineer. Over 12 intensive weeks you’ll translate product goals into SLIs/SLOs, draft cost-aware architecture diagrams, and implement blue-green and canary roll-outs with Argo Rollouts. From feature stores and data lineage to drift dashboards and GDPR audit trails, you’ll learn the frameworks that underpin reliable, scalable machine-learning in production—then prove it with a peer-reviewed design doc and monitored service.

    • Translate product goals into ML feasibility studies, SLIs, SLOs, and target latency/throughput

    • Build a cost model comparing serverless, VM, and GPU cluster options

    • Stand up Feast or Delta Feature Store; guarantee offline/online parity

    • Enforce data tests & freshness SLAs with Great Expectations and OpenLineage

    • Draft high-level + low-level diagrams, scaling strategies, and failure modes

    • Run design-doc reviews, apply RFC feedback loops used at FAANG and scale-ups

    • Implement blue-green, canary, and shadow deployments via Argo Rollouts & Istio

    • Automate CI/CD with GitHub Actions, MLflow Registry, and Terraform IaC

    • Instrument services with Prometheus + Grafana; set alert thresholds

    • Add drift detection, rollback playbooks, and audit trails for SOC-2 / GDPR

    • Team project: write a 10-page design doc & ship a costed, monitored ML service

    • Present to senior engineers & recruiters; receive actionable feedback and reference letter

Who Should Enrol?

  • You’re shipping models already but need to design production-grade pipelines that hit uptime, latency and cost targets.

  • Become the architect of CI/CD, feature-store and observability layers instead of the firefighter who inherits them.

  • Add modern ML reference architectures to your toolbox and learn to cost-out GPU, feature-store and inference workloads.

  • Move from notebooks and PoCs to designing services that scale, auto-retrain and pass compliance audits.

  • Fast-track into a high-leverage ML systems role by mastering design docs, trade-off analysis and platform tooling.

Engineers and data scientists who must own the architecture, reliability, and cost of ML solutions—not just the code.

Prerequisites
Python & Git fluency, basic ML deployment experience. Our free Systems Thinking Sprint and Docker-K8s Mini-Camp bridge badges are included for all Intermediate-track graduates.

Career Pathways

  • Design end-to-end ML platforms that balance accuracy, latency, security and cost.

  • Own critical ML services, mentor teams and define technical direction.

  • Build observability, alerting and automated rollback for model pipelines at scale.

  • Integrate feature stores, vector DBs and auto-retraining into cohesive, cloud-native stacks.

  • Translate business goals into costed ML architectures and roadmaps that engineering can execute.

You’ll graduate with a publish-ready design doc, a costed architecture, and a live, monitored ML service—matching the deliverables senior-level job ads demand.

Amir Charkhi
Technology leader | Adjunct Professor | Founder

With 20 + years across energy, mining, finance, and government, Amir turns real-world data problems into production AI. He specialises in MLOps, cloud data engineering, and Python, and now shares that know-how as founder of AI Tech Institute and adjunct professor at UWA, where he designs hands-on courses in machine learning and LLMs.

Advanced: ML System Design

12 Weeks. Live Online Classes. Next Cohort 2nd September

Advanced: ML Systems Design Advanced: ML Systems Design
Quick View
Advanced: ML Systems Design
$4,250.00

  • “In just six weeks I went from zero Python to building a churn-prediction model I could demo at work. I finally feel job-ready.” — Maddie, Data Analyst

  • “The pace was perfect—hands-on from day one and always tied back to real business value. Best learning experience I’ve had online.” — Demian, Mining Engineer

  • “Being a new mum, flexible recordings were a lifesaver. I could pause, practice, and still keep up with the cohort.” — Molly, Graduate Analyst

  • “Clear path: beginner foundations, then straight into their Intermediate Data Science track. No other provider lays out the journey so cleanly.” — Bec, Business Analyst

  • “Projects mirrored the stack we use at work—no toy examples. I deployed my capstone on AWS the same night.” — Amy, Cloud Architect

  • “From pandas to production MLOps in 24 weeks—and I never felt lost. The Slack support channel replies within minutes.” — Arjun, Energy Sector Intern

Frequently Asked Questions

  • Beginner courses: none— we start with Python basics.
    Intermediate & Advanced: ability to write simple Python scripts and use Git is expected.

  • Plan on 8–10 hours: 2× 3-hour live sessions and 2–4 hours of project work. Advanced tracks may require up to 10 hours for capstone milestones.

  • All sessions are recorded and posted within 12 hours. You’ll still have access to Slack/Discord to ask instructors questions.

  • New intakes launch roughly every 8 weeks. Each course page shows the exact start date and the “Apply-by” deadline.

  • Just a laptop with Chrome/Firefox and a stable internet connection. All coding happens in cloud JupyterLab or VS Code Dev Containers—no local installs.

  • Yes. 100 % refund until the end of Week 2—no questions asked. After that, pro-rata refunds apply if you need to withdraw for documented reasons.

  • Absolutely. We issue invoices to companies and offer interest-free 3- or 6-month payment plans.

  • Live Q&A in every session, 24-hour Slack response time from instructors, weekly office-hours, and code reviews on your GitHub pull requests.