Quest 6 of 15
Foundation Project Checkpoint: Build & Evaluate an ML Application
Integrate the first five modules into a foundation project: problem definition, dataset, model choice, training, evaluation, and a simple inference path. This is a checkpoint, not the final production capstone: later modules add deeper mathematics, feature pipelines, computer vision, NLP, deployment, responsible AI, and agents before the portfolio capstone in module 15.
Start here
This foundation project checkpoint turns the first five modules into one engineering system. A notebook with a high score is not enough: another person must be able to understand the problem, reproduce training, run prediction, inspect limitations, and decide whether the result is suitable for use. Later modules extend this work before the final Module 15 portfolio capstone.
Big idea
A production-style ML project connects a clear problem statement to versioned data, reproducible training, honest evaluation, a tested inference path, monitoring assumptions, and documentation. Scope one complete vertical slice rather than leaving five ambitious features half-built.
Learn one idea at a time
Read, explore, then mark each idea when you can explain it.
Idea 1 of 9
Write the decision context first: who uses the output, what action it supports, what one prediction represents, and what a costly error looks like. Choose a metric and simple baseline before training. If you cannot explain how a better metric improves the user’s workflow, the project is not framed clearly enough.
Choose a deep dive
Open the topics you want to explore. The detail stays folded until you need it.
Deep dive 1Worked example: support-ticket routing service
A developer trains a text classifier to route tickets to billing, account access, or technical support. The repository includes a versioned label map, preprocessing pipeline, baseline, per-class metrics, FastAPI endpoint, request validation, and tests. Low-confidence tickets go to a general queue rather than being forced into a category. The README explains that the model does not determine ticket urgency and must be retrained when product categories change.
Deep dive 2A strong demo includes failure
During presentation, show one correct prediction, one difficult case, and one rejected input. Explain what evidence led you to choose the model, where it fails, and what you would collect next. This demonstrates that you understand the system boundary. Employers and reviewers learn more from an honest failure analysis than from a single polished accuracy number.
Deep dive 3Reproducibility is a feature
Random seeds, pinned deps, and a single command to train make your capstone credible to reviewers and employers.
Deep dive 4Monitoring hooks early
Log input distribution hashes and prediction confidence — cheap hooks now save firefighting after launch.
Deep dive 5Case study: anatomy of a strong capstone repo
A reviewer opens your repository and, in ninety seconds, should find: a README stating the problem, the data source, the headline metric with its baseline comparison, and a single command to reproduce training. A notebook or report with honest error analysis — including the slices where the model is weak. A serving path (even a simple FastAPI endpoint) that applies the same preprocessing as training, with a health check and an example request. Pinned dependencies and a fixed seed. A limitations section written like an engineer: 'degrades on inputs longer than X; not evaluated on Y; would monitor Z in production.' This structure signals judgment, which interviews probe far harder than model choice.
Deep dive 6The gap between a model and a product
A trained model becomes a product only after questions that have nothing to do with architecture: What latency can the caller tolerate, and does that rule out your ensemble? What happens on malformed input, missing features, or a model-server timeout — is there a sensible default or a graceful error? Who retrains when drift arrives, triggered by what alert, validated how, and rolled back how quickly? Where do predictions get logged for the day a customer disputes one? Teams that answer these in the design document ship systems; teams that answer them in production incidents ship apologies. Your capstone gains more credibility from addressing three of these honestly than from two extra points of accuracy.
Deep dive 7Ship end-to-end
README, metrics, predict path, limitations — employers read all four.
One-minute challenge
Connect this lesson to real life
Name one situation where this idea could help, and one thing a person should still check.
Explore a real-world example
Use the arrows to connect the idea to a visible situation.
Photo example
Example: model card
State intended use and known failure modes.
Key terms
Tap a term to flip and read the definition.
Optional further learningFree textbooks and trusted online resources
These sources informed the course structure. Use them to revisit a concept or study it in more depth.
Ready check
Tick each idea only when you could explain it without looking back.
Ready for practice? Complete capstone milestones: proposal, train/eval, serve/demo.
Extra context (audience, logistics, curriculum notes)
Built for: Individual project; mentor feedback via chat lab optional.
Formats: Capstone milestones · Code review · Demo · Written report
Developer capstone — end-to-end ML application with evaluation and deployment sketch.
Next up
Ready for the next part?
When you've finished the reading, inline exercises, and knowledge check for this part, check the box to continue.