Why Every ML Engineer Should Practice Explaining Their Portfolio Aloud

"So my first project was the Iris classifier, where I learned
scikit-learn. Then I did Titanic survival prediction. After
that I tried house prices with linear regression. Then I did a
CNN on MNIST, and then a sentiment project, and then a churn
model which I deployed with Flask, and also a recommendation
system..."

(The best project, the deployed churn model, appears sixth.
The interviewer's attention left at the second.)

"My portfolio is mostly about taking models beyond the
notebook.

The project I'd start with is a churn model that I served as
an API, with input checks and a simple monitoring script.

Before that, I built a demand forecast where I learned
time-based validation the hard way, after a random split gave
me misleading results.

Most recently, I've been working on a text classifier for
support tickets, focused on keeping inference fast.

Happy to go deep on any of these. The churn API is probably
the most relevant to this role."

# List your repos with descriptions and last update
# (GitHub CLI; replace YOUR_USERNAME)
gh repo list YOUR_USERNAME --limit 50 \
  --json name,description,updatedAt,isFork \
  --jq '.[] | [.name, .updatedAt[:10], .description] | @tsv'

# Check each pinned repo has a README with results
for r in churn-api demand-forecast ticket-classifier; do
  echo "== $r"
  gh api repos/YOUR_USERNAME/$r/readme --jq '.name' \
    || echo "NO README"
done

## Hi, I build ML models that run outside notebooks

What I work on:
- Serving models as APIs and batch jobs
- Honest evaluation: baselines, time-based splits
- Simple monitoring for inputs and predictions

Start here:
- churn-api: churn model served with FastAPI, input checks,
  drift script
- demand-forecast: weekly forecast with time-based
  validation and error analysis by store
- ticket-classifier: support ticket routing, TF-IDF baseline
  vs small transformer, latency measured

Currently learning: model versioning and CI for ML

For each project, answer in one line:

PROJECT        NEW THING IT TAUGHT ME
-------------  -------------------------------------------
_____________  ____________________________________________
_____________  ____________________________________________
_____________  ____________________________________________

Now look for the common thread. Typical ones:
- getting models into real use
- messy, real-world data
- a domain (health, retail, finance, agriculture)
- a modality (text, images, time series)
- small, fast, efficient models

Write your one-sentence opener:
"My work is mostly about ____________________."

End the tour with direction, not a trailing 'so yeah':

"Across these, the common thread is getting models out of
the notebook and keeping them reliable. The next thing I'm
learning is model versioning and automated retraining, which
is a big part of why this role interests me, since your team
runs models in production.

Which project would you like me to go deeper on?"

DAY 1  Audit: list all repos, pick three, unpin the rest
DAY 2  Write the through-line sentence and one link per project
DAY 3  Record the two-minute tour. Listen back.
DAY 4  Fix READMEs so they match what you say aloud
DAY 5  Prepare role-specific orders for two job descriptions
DAY 6  Record the thirty-second version
DAY 7  Give the tour to a friend; they pick one project and
       ask five follow-up questions

OPENER (10 s)
"My work is mostly about ________________."

LEAD PROJECT (45 s)
"The one I'd start with is ________, where I ________.
 The result was ________, and the hardest part was ________."

SECOND PROJECT (30 s)
"Before that, ________ taught me ________."

THIRD PROJECT (30 s)
"Most recently, ________, focused on ________."

CLOSE (15 s)
"Next I'm learning ________, which is why this role ________.
 Which one would you like me to go deeper on?"

After each recording:

[ ] I opened with a through-line, not a project name
[ ] My lead project fits this specific role
[ ] I mentioned three projects, not all of them
[ ] Each project had one 'what it added' line
[ ] I spoke about problems before tools
[ ] Total time was close to two minutes
[ ] I closed with direction and a question
[ ] My pinned repos match the order I used