← Back to the catalogueMLOps
Classroom . Online . Hybrid
Mode
Course overview
An operations course for machine learning systems: packaging, shipping, monitoring and retraining models. The course treats a model as one component of a maintained service. It covers data and model versioning, experiment tracking, pipelines, containerisation, deployment patterns, observability, drift detection and retraining triggers.
Learners take one trained model through the full lifecycle on a cloud account, including automated retraining.
Who it is for
- ML engineers and data scientists
- DevOps engineers supporting data teams
- Backend developers deploying models
- Platform and infrastructure teams
Prerequisites
Python, a trained model of your own, Git and basic Linux. Docker exposure is helpful.
How it runs
Learn → Practise → Build → Experience → Demonstrate, ending in a capstone. Delivered by practitioners from the engineering bench, in Madurai, Coimbatore and online.
Final project
Full Lifecycle for One Model
Programme sheet
The printed sheet carries the full module breakdown, labs, project work and certification path. Fees, dates and formats for the next intake are confirmed by the education team on enquiry.
Learning outcomes
01
Version datasets, code and models reproducibly.
02
Package training and inference in containers.
03
Serve models for batch and real-time use.
04
Trigger and validate retraining safely.
05
Track experiments and register model versions.
06
Automate training and deployment pipelines.
07
Monitor latency, cost, data drift and quality.
Module structure
8 modulesModule 01
MLOps Foundations
ML lifecycle . Team roles . Reproducibility . Technical debt in ML . Maturity levels
Git workflows . Model artefacts . Environment pinning . Data versioning with DVC . Feature stores
Lab
Versioned Training Run
Module 03
Experiment Tracking
Runs and parameters . MLflow . Model registry . KAIZEN . Metrics and artefacts . Weights & Biases . INFINITIES PRIVATE LIMITED
Training pipelines . Airflow and Prefect basics . Data validation . Orchestration . Scheduling
Lab
Automated Training Pipeline
Module 05
Containerisation
Dockerfiles for ML . GPU images . Image size and security . Dependency management
Lab
Containerised Inference . Compose
FastAPI serving . Streaming . Autoscaling . Batch scoring . Kubernetes basics
Lab
Kubernetes Deployment . Canary and Shadow Releases
Service metrics . Logging . Prometheus and Grafana . Data drift . Alerting
Module 08
Governance and Cost
Model cards . Audit trails . Retraining policy . Approval workflows . GPU cost control
Assessment & certification
Final project and review
25%
Learners who complete all modules, submit the final project and clear the review receive a course completion certificate from Kaizen Infinities Private Limited. Project work is documented for the learner's portfolio, and interview preparation is included in the closing sessions.
Career outcomes . Roles this programme prepares for
MLOps EngineerML Platform EngineerDevOps Engineer (ML)Data EngineerAI Infrastructure Engineer
