Are you ready to meet the demands of tomorrow? Evaluate your approach to skilling.

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

12 Weeks. Live Online Classes. Instructor-led.

Enrol Now

Our Partners

Logos of NVIDIA, Google Cloud Platform, AWS, Databricks, and Azure.
A diverse group of professionals attending a business meeting in a bright conference room. A woman is standing and presenting to the seated participants.

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

A woman wearing a virtual reality headset in a warehouse, with digital graphics and sparks around her hands.

Draft production design docs that balance accuracy, latency & cost

Group of young people collaborating around a computer in a modern office with blackboard wall.

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.

Black and white photo of a smiling man with a beard, wearing a black Nike t-shirt against a plain background.

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.

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

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.

Student Outcomes

  • "I recently completed a course at AI Tech Institute and was thoroughly impressed. The curriculum was thoughtfully structured, the instructor was truly dedicated, and the overall learning experience delivered exceptional value."

    Maria

  • "Taking the Machine Learning course at AI Tech Institute was a game changer. It gave me the skills and confidence to break into AI and helped me land a job soon after. The content was practical and beginner friendly, with clear lessons and hands-on projects that made a real difference during interviews."

    Pani

  • "Very valuable course. Amir is a great instructor, explained how machine learning used in the real world in a easy way. Highly recommend for people who want to know more about AI or work in this area."

    Hoàng Phước

  • "The AI and Data science bootcamp was one of the most impactful learning experiences I’ve had. The curriculum was well structured. The instructor was knowledgeable and supportive, and the course provided a strong balance between theory and hands on labs. Highly recommended!"

    Ehsan

  • "I took a course at AI Tech Institute, and it genuinely exceeded my expectations. The content was not only clear and well-structured, but also incredibly relevant to the challenges I deal with day-to-day at work."

    Rosha

  • "Extremely happy with clear, well designed and applicable syllabus to be implemented in day-to-day need of nowadays coding ability. Coming from non-IT background, it is a valuable experience of seamlessly and effortless the teaching was, for me to understand the complex subjects. Would definitely recommend to colleagues and friends!"

    Kresna

  • "Absolutely fantastic experience with AI Tech Institute! I couldn’t be happier with the quality of education and support I received here. The instructors are true experts — knowledgeable, approachable, and always willing to help students succeed. The curriculum is up-to-date with the latest advancements in AI and tech, blending theory with plenty of hands-on projects that really build practical skills."

    Foad

  • "Amir is both technically proficient, and a very good teacher. He was attentive to where each person in the group was and paced the information flow accordingly. I also found him very approachable, encouraging and supportive."

    Francois

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.