Machine learning & data science
Operate an ML pipeline (train → register → serve → monitor)
MLflow/SageMaker/Vertex pipelines, model registry, batch/online serving, drift monitoring.
~25 focused hours·advanced
Tools: MLflow, Docker, FastAPI, Airflow/Prefect, Evidently/Grafana
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Track experiments (params, metrics, artifacts) with MLflow instead of spreadsheets
- Register a trained model in a model registry with versioning and stage transitions
- Package a model behind a served API (batch or online/real-time endpoint)
- Containerize the training and/or serving code with Docker for reproducibility
- Set up a scheduled or triggered retraining pipeline
- Monitor a deployed model for data drift and performance decay
- Roll back to a previous model version safely when a new one regresses
Needs first: Train and evaluate classical ML models, Containerize an application with Docker
Learn — free, link-checked
The few resources that matter
Read · intermediate · 30 min · mlflow.org
Getting Started with MLflow for ML
Official quickstart for experiment tracking and the model registry -- the exact tool named in Indian ML-engineer postings (e.g. Conga's JD). — MLflow
Read · intermediate · 40 min · ml-ops.org
ML Ops: Machine Learning Operations
Vendor-neutral reference for MLOps maturity levels, CI/CD/CT and drift monitoring concepts you'll be asked to define in system-design rounds. — INNOQ
Course · intermediate · 720 min · madewithml.com
Home - Made With ML by Anyscale
Free end-to-end course that takes one ML product from notebook to tested, packaged, served and monitored pipeline -- the closest thing to an MLOps job simulation. — Goku Mohandas / Anyscale
Practice
End-to-end pipeline for a UPI transaction fraud model
Take a fraud/anomaly detection model (can reuse the ml-fundamentals project or a public fraud dataset) and wrap it in a full pipeline: track training runs in MLflow, register the best model, serve it behind a FastAPI endpoint in a Docker container, and add a simple drift check that compares incoming feature distributions against the training set.
Done when
- MLflow tracks at least 3 training runs with params/metrics/artifacts logged and comparable in the UI
- Best model registered and loaded for serving by version/stage, not a hardcoded file path
- Dockerized FastAPI endpoint returns predictions and passes a basic load/smoke test
- A drift check script flags when incoming data statistically diverges from training data, with a documented threshold
Prove it
Evidence a recruiter can check
- Public GitHub repo with docker-compose or Dockerfile that runs end to end on a fresh clone
- MLflow tracking UI screenshots or exported run comparison in the README
- A short architecture diagram showing train -> register -> serve -> monitor
- Can explain live what happens on a rollback or a detected drift event
Interview
Questions you'll get asked
- Walk me through what happens from 'model trained' to 'model serving live traffic' in your setup
- How do you detect that a production model's performance has degraded, and what do you do about it?
- What's the difference between data drift and concept drift, and how do you monitor for each?
- How would you design a rollback strategy if a newly deployed model performs worse than the previous one?
- Batch vs online inference -- how do you decide which one a use case needs?
- What goes into a model registry entry beyond the weights file?
- How do you version datasets alongside model versions for reproducibility?