Machine Learning in Production
From notebook to deployed model — drift, monitoring, and the parts nobody teaches.
What you’ll learn
- Serving a trained model behind a real API
- Detecting data drift and concept drift in production
- A/B testing models safely
- Retraining pipelines and CI for ML
- The monitoring stack that catches problems before users do
About this course
A course for data scientists who can train models but want to ship them. We cover model serving, drift detection, monitoring, and retraining pipelines — the unglamorous engineering work that decides whether your model helps anyone. Taught by a principal data scientist running models in production.
Curriculum
6 modules · 48 lessons · 16h
1From notebook to service
2 lessons
From notebook to service
2 lessons
Serving a model is not the same as training one.
- Model serialization and loadingPreview20 min
- FastAPI for model serving25 min
2Monitoring and drift
2 lessons
Monitoring and drift
2 lessons
Knowing when your model is wrong.
- Data drift detection22 min
- Evidently for model monitoring18 min
Requirements
- Python and scikit-learn fluency
- Some experience training ML models
- Basic Docker comfort
Who this course is for
- Data scientists moving to ML engineering
- Backend engineers deploying ML models
- Anyone whose model works in the notebook but breaks in production
Your tutor
Principal Data Scientist at Swiggy · Bengaluru
ML practitioner focused on recommendation systems and forecasting. Built Swiggy’s delivery-time prediction model and the menu ranking system. Teaches ML with a strong emphasis on production realities — drift, monitoring, and the boring parts that actually matter.
Student reviews
4.9
156 ratings
Anil George
5 Sept 2024
This should be a mandatory course
Every DS bootcamp teaches you to train models. Nobody teaches you what happens after. Meera’s course fills that gap and then some.
Kavya N
22 Aug 2024
Saved my team a painful incident
We set up the drift monitoring from this course and caught a feature distribution shift two weeks before it would have degraded our recommendations.
Frequently asked questions
Do I need deep learning experience?
Is this about MLOps platforms like MLflow?
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