Why Ticker Academy

Real-time projects and labs.

You learn by building. In every job-focused program you build real-time projects: complete projects, made the way they are made at work.

Students gather around one laptop to work on a project.

In plain words

What is a real-time project?

A real-time project is a complete project, built the way it is built at work. You start from a real problem, build a working solution yourself and show the result. A working engineer checks it. You can talk about it in interviews, because you built it.

Projects

Sample projects from each program.

Sample projectp-04

A final test the customer agrees to

Problem: Nobody agreed what done means, so the work never ends.
Tools: A one-page plan, test cases from real use, a sign-off
Skills: Understanding needs, testing, clear writing
What you build: A final test agreed with the people who will use the system
Result: A clear finish line that both sides accept

checked on: clarity, and whether the customer agrees

Sample projectp-01

A small AI model you built and can explain

Problem: You use AI every day but cannot say how it produces an answer.
Tools: A small model of the kind behind chat assistants (a transformer), written and trained by you
Skills: How models read text, how they learn, training
What you build: A trained model and a short note on each choice you made
Result: You can explain how a model learns and spot when training goes wrong

checked on: it works, and you explain it clearly

Sample projectp-03

A document search tool with automatic tests

Problem: People cannot find the right answer in a large set of documents.
Tools: Search by meaning (embeddings), a search index, ranking, automatic tests
Skills: Search, testing, keeping costs low
What you build: A search tool with tests that run on every change
Result: You know how good the answers are and what each search costs

checked on: answer quality, cost per search

Sample projectp-02

A fair test and its result

Problem: Two teams disagree about whether a change worked.
Tools: A test design, statistics, a notebook that becomes a one-page report
Skills: Cleaning data, testing an idea, writing a clear answer
What you build: A one-page report with the numbers behind it
Result: A decision people can act on

checked on: a sound method and a clear answer

Sample projectp-04

An app set up inside a customer's systems

Problem: A working demo has to run where you have limited access.
Tools: The customer's cloud, their data, their release steps
Skills: Understanding needs, connecting systems, security checks
What you build: A running app and a handover document
Result: A final test agreed with a user

checked on: it runs, and the user accepts it

Sample projectp-01

An improved model and a test report

Problem: A general AI model does one narrow task badly.
Tools: Extra training on your own examples (fine-tuning), a test set, error checks
Skills: Preparing data, training, testing
What you build: An improved model and a report on where it still fails
Result: You can say when extra training is worth it

checked on: measured improvement, honest reporting

Sample projectp-02

A forecast and how far to trust it

Problem: A team wants a forecast and nobody knows how far to trust it.
Tools: A simple first version, a better model, a check on data the model has not seen
Skills: Building models, checking them, explaining errors
What you build: A model, a simple version to compare it with, and a short written answer
Result: A forecast with its limits stated

checked on: sound checks, plain writing

Sample projectp-03

An AI assistant that can take actions, with tests

Problem: A team wants an assistant that takes actions, and nobody knows when it is safe to use.
Tools: A language model that uses tools, structured answers, safety rules, a test suite
Skills: Designing tools, handling failures, testing
What you build: An assistant that passes a written set of tests
Result: A known failure rate and a plan for the rest

checked on: test coverage, behavior when things fail

How a project is checked

It works: A project that does not run is not finished.
It was checked: A working engineer checked it, and you fixed what they found.
You can explain it: One page that says what you built, what you chose and what you would change.

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Labs

Where you practice.

Labs are where you practice and build each week.

Sample

Sample: proposed training details for review. Resources and completion rules are not confirmed yet.

Environment: Python notebooks for practice, Git for saving code, and separate test projects.
Data: Public or synthetic datasets, with permission checks and no private customer information.
Compute: Small exercises on a laptop. Larger model tasks may use cloud computing after access is agreed.
Tools: Python, Jupyter, Git and SQL. Model and deployment tools depend on the program.

Apply

Choose your program. Then apply.

Every program shows the skills you learn and the job you train for.

Apply now