Not your Build towards a deployed AI product with tests and technical decisions you can explain in internship interviews.
Learn to prepare data, train a model and evaluate it before moving into more complex AI systems.
Complete a model project with a documented evaluation.
Learn how neural networks are trained and how language and vision systems represent information.
Explain your model’s inputs, outputs and limitations.
Call a model from an application. Handle structured outputs, streaming, failures and usage limits.
Build an app that validates model output and handles failed requests.
Prepare documents, retrieve relevant passages and show the evidence behind an answer.
Build a document assistant that cites sources and handles missing information.
Connect the assistant to useful functions and define what it may do automatically and what needs approval.
Demonstrate a tool-using assistant that cannot silently take an unauthorised action.
Create tests for accuracy, failure modes, latency and cost. Deploy and observe how the app behaves.
Complete a capstone with a demo, test results and a plan for monitoring failures.
Learn with guidance
Follow this path with live classes, project work and mentor feedback in AI Engineering. Build the portfolio and the understanding to explain it.
