Build the pipelines, warehouses and orchestration behind every analytics and AI system. 12 weeks, live, senior-led — finishing with a production data platform you ship.
Most data careers stall at the notebook. This is everything after it.
This program is about everything after the notebook — the pipelines, warehouses and orchestration that turn raw data into something an analytics or AI system can actually depend on.
Over twelve live weeks, a senior practitioner walks a small cohort through ingestion, transformation, modelling and orchestration on real data, finishing with a production data platform you’ve built and can defend.
No toy datasets, no lecture theatre — the work looks like the job.
Nobody owns the contract, nobody is told when it changes. We teach the engineering and the boundary around it.
A pipeline that runs once is a script. One that handles late data, re-runs safely and recovers from failure is a platform.
Partitioning, file sizes and orchestration choices decide what this costs. You learn to reason about it before you ship, not after the invoice.
Movement I · Weeks 01–04Move the dataHow data travels through an organisation, and ingestion that survives contact with reality.
Movement II · Weeks 05–08Model and orchestrateA tested transformation layer, and a graph of work that restarts sensibly.
Movement III · Weeks 09–12Run itCost, contracts, observability, and the platform you defend.
Foundations and the shape of a platformHow data actually moves through an organisation, the storage layers it lands in, and why most pipelines fail for organisational rather than technical reasons.
An architecture sketch for your own platform, and the reasons behind each layer.
IngestionBatch and incremental loading from files, databases and APIs. Idempotency, late-arriving data, and the difference between a pipeline that works and one that survives.
An incremental load that is safe to re-run and handles late data.
Modelling and transformationDimensional modelling, transformation frameworks and testing your data the way you test your code. The layer that decides whether anyone trusts your numbers.
A tested transformation layer someone else would be willing to build on.
OrchestrationScheduling, dependencies, retries and backfills. Making a graph of work run reliably and restart sensibly when it does not.
A scheduled graph that recovers from failure without you watching it.
The lakehouse in practiceOpen table formats, partitioning and the cost model underneath. Where the money goes and how to stop it going there.
A cost per run you measured, and a partitioning choice that lowered it.
Quality, contracts and observabilityDetecting the broken pipeline before the business does. Data contracts, freshness checks and alerting that people do not mute.
Freshness and contract checks that catch a break before a stakeholder does.
Your platformIngestion to serving on real data, orchestrated, tested and documented, defended in front of the cohort.
A production data platform you built, priced, and can defend end to end.
Over the last fortnight you build a platform that takes a real dataset from raw ingestion through to something an analyst or a model could depend on: incremental loads, a tested transformation layer, orchestration with retries, and freshness monitoring.
You present the architecture, the cost per run, and the decisions you reversed along the way. We care more about the reasoning than the tool choice.
Intermediate. You should be comfortable with Python and SQL before week one — not expert, but able to write a query and a script without help. Beginner AI Foundations is the usual route in.
You need a laptop you can install tooling on and a cloud account; we use free tiers where possible and warn you before anything costs money. Budget eight to ten hours a week outside sessions.
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”.
