ML Engineer Bootcamp
Train, evaluate and serve models that pass contract tests, not notebooks that run once.
ML Engineer Bootcamp is a 12-week live online bootcamp, taught on Zoom by one instructor at about 15 hours a week, with 6 deployed projects and a one-to-one every week. The founding price is A$200, down from A$400. Live sessions run on weekday evenings from 5:30 PM Sri Lanka and India time, with full-day sessions at weekends.
- Week 1Baseline ModelA scikit-learn baseline with cross-validation on a tabular dataset, in a Jupyter notebook
- Weeks 1–2The ML toolkitA tabular model with a leak-free validation scheme, deployed as an API
- Week 3Training LoopA PyTorch training loop with checkpoints and a validation curve on a small dataset
- Weeks 3–4Deep learningAn image or text model fine-tuned and deployed
- Weeks 5–6Data and pipelinesA retrainable pipeline with data checks, running on a schedule
- Week 7Model ContractA set of contract tests for input shape, output range and known cases on one model
- Weeks 7–8Evaluation and contractsA contract-test suite the model must pass to deploy, in CI
- Week 9Prediction LoggerA logging layer that stores every prediction with its inputs for later drift checks
- Weeks 9–10ServingThe capstone: a monitored model in production with retraining
- Weeks 11–12Employer brief and hiring sprintThe employer brief shipped, and demo day
- weeks, live on Zoom
- 12
- hours a week
- 15
- projects to build
- 10
- minutes 1-1, every week
- 30
Who it is for, and what you receive.
Developers and analysts with Python and some statistics who want to train, evaluate and serve models that pass contract tests, not notebooks that run once.
Python and some statistics; the first block reviews the maths you need.
- Live on Zoom
- 4h
- Recorded lessons and exercises
- 6h
- The project
- 5h
- 1
Six deployed projects
Each graded by Nova against a published rubric and signed off by the instructor at the gate.
- 2
The recorded lessons
The track's lessons and exercises, graded line by line, open for twelve months after the cohort ends.
- 3
Live on Zoom every week
A 90-minute live code review, a 60-minute squad lab, 60 minutes of office hours, and a 30-minute 1-1 with your instructor.
- 4
Your hiring plan
Opened in week 1 and reviewed in every 1-1: target roles, the gap map, the proof list, weekly actions, an interview log, the outcome.
- 5
The record
Every grade and every project with its repository at square1ai.com/u/{handle}, and the recording of your viva, verifiable by any employer.
- 6
The certificate
A credential ID that resolves at square1ai.com/verify to the real completion.
- 7
The hiring sprint
Weeks 11 and 12: CV and portfolio from graded work, applications, mock interviews scored against the role, demo day.
- 8
Nova for twelve weeks
A tutor with every submission of yours in its memory, at 2am as well as in class.
Twelve weeks in six parts.
Each part teaches for a week, then you build and deploy a project, then a gate checks it before the next part opens. The project and its gate are the part's entry on your record.
Live time each week on Zoom: a 90-minute class where the instructor reviews real submissions, a 60-minute squad lab, 60 minutes of office hours, and your own 30-minute 1-1. Nothing is lectured live; the recorded lessons do that.
- Key skillsRegression and classification, properlyFeature engineering and leakageCross-validation you can trustWeek 1
The ML toolkit
- Regression and classification, properly
- Feature engineering and leakage
- Cross-validation you can trust
- The maths you actually need
You build: A leak-free tabular model
Week 2Project 1: tabular
- Held-out evaluation
- Serving as an API
You build: A tabular model deployed as an API
Project 1 · the gate at week 2Tabular
A tabular model with a leak-free validation scheme, deployed as an API
You hand in- Model
- Validation scheme
- Deployed API
- Evaluation report
The gateHeld-out performance matches validation within a tolerance.
What you can do by week 12.
Six deployed models and pipelines, contract tests you will insist on everywhere, and the proof that you can take a model from a notebook to production and keep it honest there.
- 1
Build regression and classification models with leak-free validation you can trust.
- 2
Train and fine-tune deep models in PyTorch with reproducible runs.
- 3
Build retrainable pipelines with data versioning and quality checks.
- 4
Write contract tests a model must pass to deploy, including fairness and drift checks.
- 5
Serve and monitor a model in production with retraining triggers.
- 6
Defend a model and what happens when the data shifts.
No placement rate is shown, because there are no graduates to count yet. The roles above are what the projects are built for.
Your record at week 12.
Every exercise and project is graded by Nova against a rubric you can read, and every grade is kept on one page an employer can open and run. This is what the programme writes to it.
- Graded, line by line
- Nova reads every submission against the brief and the rubric and returns a score, what you did well and what to fix.
- Six gates
- A block does not open until the previous project passes its gate. You always know where you are and what is next.
- A weekly 1-1
- Thirty minutes with your instructor, who has already read your code before the call.
- One page an employer can run
- Every grade, project and the viva recording at /verify. An employer opens it, runs the code and watches you defend it.
- Week 2Graded, gate signed
Tabular
Held-out performance matches validation within a tolerance
- Week 4Graded, gate signed
Deep
Training is reproducible from a seed and a config
- Week 6Graded, gate signed
The pipeline
A bad data batch is rejected before training
- Week 8Graded, gate signed
Contracts
A degraded model fails the contract
The entries, not the grades: those are yours to earn. The page lives at /verify and an employer needs no account to open it.
8 entries, twelve weeks. Reserving a spot holds the founding price.
Reserve a seatHow we help you find a job.
The last block is not curriculum. It is the hiring sprint, and the proof you built in the ten weeks before it.
The whole plan on a few pages: the parts, the projects, the gates and the price. We email it to you.
Get the booklet- 1
Your hiring plan, from week 1
Six parts you and your instructor keep: target roles, the gap map from real postings, the proof to send, weekly actions, an interview log, the outcome. Read before every 1-1.
- 2
The hiring sprint
Weeks 11 and 12: CV, portfolio, applications, mock interviews, and demo day in front of hiring partners.
- 3
A record an employer can run
Your deployed projects and the recorded viva on /verify. An employer opens it, runs the code and watches you defend it.
- 4
The career agent
Paste a real job posting at /career and it maps the role to your graded work and what to do next.
- 5
The roles directory
Every role we prepare people for, what it pays and what it asks, at /roles.
Questions people ask.
About ML Engineer Bootcamp, answered from the plan on this page.
- How long does it take to learn machine learning well enough to get hired?
- How to Deploy a Machine Learning Model: Step by Step
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One cohort, one instructor. Hold your spot.
A cohort of 25, one instructor, 12 weeks, six things you ship. Spot holders hear the start date first, and nothing is charged before you confirm.
- Six deployed models and pipelines
- Contract tests you will insist on everywhere
- A recorded viva
- Your record on /verify
About a minute. No account, no card, and nothing is charged until you confirm.