MLOps: Taking Models to Production
Why colleges run this
Most machine-learning models never reach production; MLOps skills close that gap and are in acute demand as organisations operationalise AI.
Course outcomes
CO1
Describe the MLOps lifecycle and common failure modes
K2CO2
Apply versioning to data, code and models for reproducibility
K3CO3
Package and serve a model behind an API
K3CO4
Build a reproducible training-to-deployment pipeline
K3CO5
Monitor a deployed model for drift and performance
K4Modules tap a module for its theory & lab
Hours shown are the recommended 3-day format — module time scales to the duration you pick.
01MLOps foundations4 h
Theory
The ML lifecycle, why models fail in production
Lab
Map the lifecycle and its failure points
Output
Lifecycle map
02Versioning4 h
Theory
Data, code and model versioning
Lab
Version a dataset, code and model
Output
Versioned project
03Packaging and serving4 h
Theory
Containerised model serving behind an API
Lab
Serve a model behind an API
Output
Served model
04Pipelines4 h
Theory
Reproducible training and deployment pipelines
Lab
Build a reproducible pipeline
Output
Pipeline definition
05Monitoring4 h
Theory
Performance, drift, alerting
Lab
Add monitoring and alerts
Output
Monitoring dashboard
06MLOps capstone4 h
Theory
A model deployed behind a monitored API
Lab
Deploy the capstone model
Output
Monitored deployed model
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment