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End-to-End ML Pipeline
Intermediate⏱ 7–10 days · 6 milestones
Build a full ML pipeline — data ingestion, feature engineering, model training, evaluation, and deployment — with experiment tracking.
Difficulty
Intermediate
Duration
7–10 days
Milestones
6 steps
Interview Qs
3 questions
🛠 Tech stack
Pythonscikit-learn or PyTorchMLflowFastAPIDockerGitHub Actions
✅ Prerequisites
- ● Python (intermediate)
- ● Basic ML
- ● SQL
Step-by-step milestones
1
Data ingestion + EDA
Load data, analyze distributions, handle missing values.
2
Feature engineering
Create features, encode categoricals, scale numerics.
3
Model training + tracking
Train models, log metrics to MLflow.
4
Model evaluation
Evaluate with appropriate metrics, analyze errors.
5
Serve with FastAPI
Build a REST API for predictions.
6
Containerize + CI/CD
Dockerize, add GitHub Actions for testing.
Skills you'll build
ML EngineeringFeature engineeringModel evaluationMLOpsExperiment tracking
📝 Interview questions you'll face
- 1.How do you handle data drift in production?
- 2.What metrics did you choose and why?
- 3.How would you retrain automatically?
🌟 Portfolio guidance
Include MLflow experiment screenshots and a clear model card. Use a real dataset from Kaggle.