Ship and operate real AI — the production half of the craft. Pipelines, serving, evals, guardrails, monitoring and agentic coding, for engineers taking models and LLM apps from prototype to dependable.
Building a model is the easy part. Keeping it working in production is the job.
This is the “ship & operate” half of our technical spine — the engineering that turns a prototype into something a business can depend on.
Twelve live weeks across pipelines, serving, evaluation, guardrails, monitoring and the agentic-coding workflow reshaping how AI gets built. It absorbs what used to be our separate ML/MLOps, data-engineering and generative-AI tracks into one coherent production course.
Senior-led. Small cohort. Real systems — not toy datasets and not a slide deck about best practice.
Not because the modelling was wrong — because nothing around it was built to be run by someone else, twice.
No stack trace. A distribution shifts, a prompt drifts, quality degrades for weeks before anyone notices. You learn to instrument for that.
Half of this course is teaching you to prove a change was an improvement, offline and online, before you defend it to anyone.
Movement I · Weeks 01–05ShipGet something real out of the notebook and standing up on its own.
Movement II · Weeks 06–08ProveShow — with evidence, not vibes — that it works and that changes improve it.
Movement III · Weeks 09–12OperateKeep it working when it degrades quietly, and hand it over without fear.
From notebook to servicePackaging, dependencies, configuration and the first deployable artefact. Where most prototypes die and why.
A containerised service that builds clean — running from a repo, not your laptop.
Pipelines and feature workReproducible data and feature pipelines, versioning, and training that someone other than you can re-run.
A pipeline a colleague can run and get your numbers back.
ServingBatch, real-time and streaming inference. Latency budgets, batching, and what changes when a model sits behind an API.
A latency budget you can defend and a serving choice that meets it.
Evaluation in productionOffline evals, online metrics, shadow deployments and A/B tests. Knowing whether the thing you shipped is actually better.
An eval suite that separates a real improvement from a coincidence.
LLM applications and guardrailsRetrieval, structured output, and the guardrails that keep a language model inside its remit.
Retrieval and structured output under guardrails that hold against hostile input.
Monitoring and driftInstrumenting a system that degrades quietly. Alerts that fire on the right things and the runbook that follows.
Alerts on the signals that precede failure — and the runbook for when they fire.
The agentic coding workflowHow AI coding agents are changing the engineering loop, used properly — scoping, review and safety inside a real codebase.
An agent workflow with scoping and review your team can actually adopt.
Your production systemDeployed, evaluated, monitored and documented, then defended in front of the cohort.
The system, the evidence, and an honest post-mortem — defended in the room.
You take a model or an LLM application from prototype to something operable: deployed behind an interface, evaluated offline and online, monitored for drift and cost, with a runbook for the failure you consider most likely.
You present the system, the evaluation evidence, and an honest incident post-mortem from something that broke during the build. We ask everyone for the post-mortem; the ones who have nothing to report usually have not pushed hard enough.
Intermediate to advanced. You should be a working engineer or have completed AI & Data Science: comfortable Python, the command line, Git, and at least passing familiarity with containers.
You need a cloud account and a laptop you control. Expect ten hours a week outside sessions — this is the most demanding of the twelve-week programs.
Frameworks are great. But understanding what they abstract — the loop, the parsing, the failure modes — is what lets you actually debug and design them under pressure.
Amir founded, funds and directs AI Tech Institute, and still teaches. He holds a PhD and has spent more than twenty years building and leading AI, machine learning and data teams in Australian industry — the kind of work the programs here are drawn from rather than adapted to.
He currently leads asset performance and analytics at Synergy, Western Australia's state-owned electricity generator and retailer. Before that he led GenAI and MLOps program delivery at EY, working on platform modernisation for tier-one Australian banks; served as Chief AI Engineer at Hancock Prospecting, where he built enterprise GenAI platforms for executive decision support, led a Snowflake migration, stood up on-premises NVIDIA H100 infrastructure and Kubernetes clusters for production AI, and built a team of twelve engineers; and spent nearly five years as Tech Lead for ML and Data Science at Woodside Energy.
He has been teaching for twenty-one years. He is an Adjunct Associate Professor at the University of Western Australia, teaching quantitative analysis and decision-making at master's level, and holds Databricks University Alliance Faculty status and the AWS Certified AI Practitioner certification. He has published peer-reviewed research in Exploration Geophysics on machine learning for reservoir characterisation, and writes and records openly about the parts of AI engineering most courses skip — agent loops, termination logic and failure modes.
Cohorts are deliberately small so every piece of work gets reviewed properly and every presentation gets heard in the room. If an intake is full we’ll say so and hold your place for the next one — we won’t quietly oversell a cohort.
See all upcoming dates →The prerequisites for this program are in “Who it’s for” above. Our foundation-level programs and workshops assume none. If you’re unsure, ask on a qualification call — we would rather redirect you than take your money for the wrong cohort.
The commitment for this program is shown in the hero and in “Who it’s for”. Cohort programs run live sessions plus practice between them; workshops are a single block with no homework.
The work you shipped — defended in front of the cohort — and an honest recommendation on your next step. Sometimes that is another cohort with us, sometimes it isn’t; we will tell you which.
No. AI Tech Institute is not a Registered Training Organisation and this is not a nationally accredited qualification. It is professional education, assessed on the work you ship, with a certificate of completion from us. We won’t claim accreditation we don’t have.
Yes — we invoice organisations directly and can supply a scope and outcomes summary for your L&D or capability budget. If three or more people from one team want in, talk to us about a private cohort instead.
Full refund any time before the program starts. Once it has started we can’t refund the place, but we will carry you into a later intake where the circumstances warrant it. Ask us — we’re reasonable.
Thirty minutes with us and you’ll know whether this is your right next step — or which of our programs is. No pitch, no payment, and we will say so if the answer is “not this one”.
