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Build Real-World AI Solutions & Deploy at Scale

12 Weeks. Live Online Classes.

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Master Prompt Engineering & RAG

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Develop ML & Deep Learning models

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Deploy scalable AI services and APIs

What you will learn?

  • • Refresh advanced SQL & feature engineering, then wire them into a Feature Store using Feast.
    • Track every experiment with MLflow; set up reproducible conda/poetry environments.
    • Design AB-style offline/online parity tests to avoid train-serving skew.

  • • Build modular pipelines with scikit-learn, XGBoost, and Optuna hyper-parameter sweeps.
    • Refactor notebooks into Python packages; enforce type-safety with Pydantic.
    • Spin up a lightweight CI workflow on GitHub Actions that runs unit tests & smoke models on every PR.

  • • Fine-tune Transformer models on Hugging Face; plug in hosted models from OpenAI / AWS Bedrock / Azure OpenAI.
    • Master embeddings, vector search, and RAG architecture with pgvector / Pinecone / Chroma.
    • Develop robust prompt chains with LangChain and evaluate them with guard-rails for bias & toxicity.

  • • Wrap models in FastAPI & async endpoints, auto-documented with OpenAPI / Swagger.
    • Add caching & rate-limiting, secure with JWT & OAuth.
    • Publish a versioned Python client SDK so front-end and mobile teams can integrate in hours—not days.

  • • Containerise services with Docker, use multi-stage builds for 70 % smaller images.
    • Deploy to Kubernetes (EKS, AKS, or GKE) with Helm charts; leverage KEDA for event-driven auto-scaling.
    • Roll out blue-green & canary releases via Argo Rollouts; monitor latency & drift with Prometheus + Grafana.

  • • Team project: deliver a production-ready AI service (e.g., real-time churn prediction + LLM explainer) accessible at a public HTTPS endpoint.
    • Produce an architecture doc, cost-of-ownership analysis, and post-mortem run-book.
    • Pitch to a panel of hiring managers & receive a written reference you can attach to applications.

Bridge the gap between notebooks and revenue-generating AI services. Over 12 weeks you’ll refactor ML code into modular packages, fine-tune transformers for Gen-AI features, and deploy FastAPI micro-services in Docker and Kubernetes with autoscaling. MLflow tracking, LangChain RAG pipelines and KEDA cost controls round out the stack. Graduates leave with a live, documented API and the skills employers demand for AI Engineer, ML Engineer or MLOps-heavy product teams.

Who Should Enrol?

Software & data professionals ready to turn notebooks into cloud-scale AI products.

Prerequisites
Solid Python, basic ML knowledge (regression/classification), comfort with Git. Completion of our Python & Git Kick-start plus ML & Cloud First Look (or equivalent) is recommended.

  • Moving toward ML-powered micro-services.

  • Who can train models but need deployment & DevOps discipline.

  • Tasked with owning machine-learning workloads.

  • Aiming for an AI Engineer or MLOps Engineer role within a year.

Career Pathways

  • Design, train, and deploy ML & Gen-AI models; own APIs that power product features.

  • Design, train, and deploy ML & Gen-AI models; own APIs that power product features.

  • Build RAG systems, fine-tune LLMs, and integrate guard-rails for safe enterprise use.

  • Automate CI/CD for models, orchestrate on Kubernetes, monitor drift & performance in production.

  • Collaborate with PMs to translate user problems into AI micro-services with measurable ROI.

  • Architect scalable, cost-efficient AI solutions on AWS, Azure, or GCP; advise on best-practice IaC & security.

Graduates leave with a portfolio, GitHub repo, and recruiter-friendly talking points aligned to entry-level requisitions

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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.

Intermeidate: AI Engineering Course

12 Weeks. Live Online Classes.

Intermediate: AI Engineering Intermediate: AI Engineering
Quick View
Intermediate: AI Engineering
$4,500.00

Bridge the gap between notebooks and revenue-generating AI services. Over 12 weeks you’ll refactor ML code into modular packages, fine-tune transformers for Gen-AI features, and deploy FastAPI micro-services in Docker and Kubernetes with autoscaling. MLflow tracking, LangChain RAG pipelines and KEDA cost controls round out the stack. Graduates leave with a live, documented API and the skills employers demand for AI Engineer, ML Engineer or MLOps-heavy product teams.

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.