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Technology track

MLOps internship

Take models from a notebook to a service that stays reliable. Courses from SkillTank Academy, live tasks reviewed by KaizenSpark Tech mentors.

Apply for MLOps Programmes & fees

Prerequisites

Python
Command line basics
Some ML exposure

Tools you’ll use

DockerFastAPIGitHub ActionsMLflow

Roles it prepares you for

MLOps intern
DevOps intern
ML platform engineer intern

Syllabus

The MLOps track, week by week.

Shown for the 3-month internship. The 1-month internship covers the first phase in depth; the 6-month internship extends each phase with a specialisation course and module ownership.

Weeks 1–2 · Foundations

Linux, environments and Docker
Reproducible training runs
Experiment tracking

Weeks 3–6 · Build

Serving models behind an API
CI/CD pipelines for ML
Automated tests for data and models

Weeks 7–10 · Own

Monitoring, logging and drift
Rollbacks and versioning
Cost awareness

Weeks 11–12 · Graduate

Runbooks and on-call basics
Architecture notes
Platform demo

Deliverables

What you’ll hand in.

Every deliverable is reviewed and rated on the 1–5 quality rubric, and approved work is added to your record.

A containerised model service
A CI/CD pipeline with tests
A monitoring dashboard and runbook

Competencies assessed

Docker and environments
Model serving APIs
CI/CD for ML
Monitoring and drift

Sample project

Package and deploy a model as an API with automated tests and monitoring.

Apply for the MLOps track.

Choose your programme level on the form. We confirm your track after a short interview.