ML · Internship domain

Machine Learning

From data pipelines to production-grade predictive models.

Apply for Machine Learning
By the end, you'll have shipped

an end-to-end ML pipeline with a live model endpoint

Program structure

Your training passport

Every domain runs the same three-stage structure. Here's what each stage looks like for Machine Learning.

STAGE 01 Week 1–2 · Training

Pipeline and baseline

Weeks 1-2. You build a clean training pipeline (data validation, feature store, baseline model) on a real dataset. Mentor won't sign off on Week 2 unless your pipeline runs end-to-end without manual steps.

WEEK
1–2
STAGE 02 Week 3–4 · On-the-Job Training

Tune and validate properly

Weeks 3-4. Hyperparameter sweeps, cross-validation discipline, error analysis on the test set. You learn why 92% accuracy on a toy dataset doesn't matter — and what does.

WEEK
3–4
STAGE 03 Month 2–3 · Development

Deploy and monitor

Months 2-3. FastAPI endpoint, Docker image, CI/CD, model drift dashboard. The project is deployed publicly so your mentor can hit it and watch the predictions change.

MONTH
2–3
What Batch 1 shipped in this domain
ML Customer-churn prediction pipeline (92% accuracy on holdout, deployed on HF Spaces)
ML Resume screener for an HR-tech partner (trained on 12k anonymised CVs)
ML House-price predictor with feature drift monitoring
Skills you'll build
  • Data cleaning & feature engineering
  • Supervised & unsupervised learning
  • Model evaluation & tuning
  • MLOps basics — CI/CD for models
  • Deploying with FastAPI / Docker
  • Monitoring model drift
Courses for this domain

Once you're enrolled as an intern in Machine Learning, two domain-matched courses unlock on your dashboard, alongside the two courses free to every intern.

Browse the course catalog

Ready to start?

Applications are open all year — there's no fixed intake date. Onboarding starts within two weeks of approval.

Apply for Machine Learning