Courses / Live bootcamps

Live bootcampTaught live on Zoom, one instructor

ML Engineer Bootcamp

Train, evaluate and serve models that pass contract tests, not notebooks that run once.

weeks, live on Zoom: 12
hours a week: 15
deployed projects: 6
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.

Before you start

Python and some statistics; the first block reviews the maths you need.

Skills

Python
scikit-learn
PyTorch
pipelines
MLOps
monitoring

A week, about 15 hours

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 blocks.

Each block teaches for a week, then you build and deploy a project, then a gate checks it before the next block opens. The project and its gate are the block's record entry.

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.

1
The ML toolkit
Weeks 1 to 2
Week 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 2
Project 1: tabular
- Held-out evaluation
- Serving as an API
You build: A tabular model deployed as an API
Project 1, the gate at week 2
Tabular
Predict loan default on a public dataset without leaking the future.
You hand in
- Model
- Validation scheme
- Deployed API
- Evaluation report
The gate
Held-out performance matches validation within a tolerance.
2
Deep learning
Weeks 3 to 4
Week 3
Deep learning
- PyTorch from the tensor up
- Training loops, schedules, regularisation
- Transfer learning
You build: A fine-tuned image or text model
Week 4
Project 2: deep
- Seeds and configs
- Reproducible training
You build: A deep model fine-tuned and deployed
Project 2, the gate at week 4
Deep
Fine-tune an image or text model for a real classification task.
You hand in
- Training code with seed and config
- Checkpoints
- Deployed endpoint
The gate
Training is reproducible from a seed and a config.
3
Data and pipelines
Weeks 5 to 6
Week 5
Data and pipelines
- Feature stores and data versioning
- Training pipelines
- Data quality checks
You build: Retrainable pipeline; squads form
Week 6
Project 3: the pipeline
- Rejecting bad batches
- Scheduling
You build: A retrainable pipeline with data checks on a schedule
Project 3, the gate at week 6
The pipeline
Retrain on a schedule and refuse bad data.
You hand in
- Pipeline
- Data checks
- Versioned features
- Schedule
The gate
A bad data batch is rejected before training.
4
Evaluation and contracts
Weeks 7 to 8
Week 7
Evaluation and contracts
- Metrics that match the business
- Contract tests for models
- Fairness and drift
You build: Contract-test suite
Week 8
Project 4: contracts
- Degraded models
- CI gates
You build: A contract suite the model must pass to deploy
Project 4, the gate at week 8
Contracts
Write the tests a model must pass to ship.
You hand in
- Contract suite
- Fairness and drift checks
- CI integration
The gate
A degraded model fails the contract.
5
Serving
Weeks 9 to 10
Week 9
Serving
- Serving patterns and latency
- Monitoring predictions
- Retraining triggers
You build: Serving and monitoring design
Week 10
Project 5 and the viva
- Production with retraining
- Defending against drift
You build: A monitored model in production with retraining; the viva
Project 5, the gate at week 10
Production
Serve the model, watch it, retrain it.
You hand in
- Serving
- Prediction monitoring
- Retraining trigger
- Runbook
The gate
The viva: defend the model and what happens when the data shifts.
6
Employer brief and hiring sprint
Weeks 11 to 12
Week 11
Employer brief
- A real problem from a hiring partner, in squads
- A partner's ML problem
- Working to someone else's definition of done
You build: The employer brief in progress
Week 12
Hiring sprint
- CV and portfolio built from graded work
- Applications and follow-ups in the hiring plan
- Mock interviews scored against the role
- Demo day
You build: Project 6 delivered; demo day
Project 6, the gate at week 12
Employer brief
A partner's ML problem.
You hand in
- The brief delivered
- Demo day
The gate
The brief's owner accepts the result.

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.

Roles this prepares you for

Machine learning engineer
ML engineer (applied)
Data scientist (production)
MLOps engineer

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.

Record, ML Engineer Bootcamp

Example

Week 2
Tabular
Held-out performance matches validation within a tolerance
Graded, gate signed
Week 4
Deep
Training is reproducible from a seed and a config
Graded, gate signed
Week 6
The pipeline
A bad data batch is rejected before training
Graded, gate signed
Week 8
Contracts
A degraded model fails the contract
Graded, gate signed
Week 10
Production
A viva: defend the model and what happens when the data shifts
Graded, gate signed
Week 12
Employer brief
The brief's owner accepts the result
Graded, gate signed
Weeks 1 to 12
Twelve 1-1 notes
What your instructor saw in your work each week, and what you agreed to do next.
Kept
Week 12
Your hiring plan and its outcome
Target roles, the gap map, applications, interviews and where you landed.
Kept

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. One email when applications open.

How 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.

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 six 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.

One email when applications open.

Fifty seats, one instructor, twelve weeks. The waitlist hears the date and the price first, and nothing is charged before the cohort exists.

ML Engineer Bootcamp

Who it is for, and what you receive.

Twelve weeks in six blocks.

The ML toolkit

Tabular

Deep learning

Deep

Data and pipelines

The pipeline

Evaluation and contracts

Contracts

Serving

Production

Employer brief and hiring sprint

Employer brief

What you can do by week 12.

Your record at week 12.

How we help you find a job.

One email when applications open.