Advanced Analytics and Model Deployment
Why colleges run this
Beyond a first model lie the techniques that make analytics trustworthy in production; ensembles, interpretability and monitoring are what employers actually require.
Course outcomes
CO1
Build ensemble models using boosting methods
K3CO2
Select and rank features by importance
K4CO3
Explain a model's predictions using interpretability techniques
K4CO4
Deploy a model with monitoring in place
K3CO5
Detect and respond to model drift
K4Modules tap a module for its theory & lab
Hours shown are the recommended 3-day format — module time scales to the duration you pick.
01Advanced modelling4 h
Theory
Ensemble methods, boosting
Lab
Build boosted ensemble models
Output
Ensemble notebook
02Feature engineering at depth4 h
Theory
Selection, importance
Lab
Select and rank features
Output
Feature report
03Interpretability4 h
Theory
Explaining model predictions
Lab
Explain predictions with interpretability tools
Output
Explanation notebook
04Deployment4 h
Theory
Serving a model with monitoring
Lab
Deploy a model with monitoring
Output
Deployed model
05Monitoring and drift4 h
Theory
Detecting and responding to drift
Lab
Detect drift and respond
Output
Drift-response note
06Capstone4 h
Theory
A deployed, monitored, interpretable model
Lab
Assemble the capstone
Output
Deployed interpretable model
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment