Foundation first, then the real work.
The track runs 8 months. It opens with one foundation month (Basic IT and Spoken English) so nobody starts the core curriculum on the back foot. The remaining seven months are the track itself.
Basic IT & Spoken English
- ▪Computer & Windows fundamentals, files and folders
- ▪Microsoft Office: Word, Excel and PowerPoint
- ▪Email, Google Workspace and safe internet habits
- ▪Everyday spoken English, pronunciation and fluency drills
- ▪Presentations, interviews and workplace communication
Cloud + MLOps, in depth
- ▪The complete Cloud + MLOps curriculum, taught phase by phase
- ▪Trainee → Intern → Shadow → Expert progression
- ▪Paid internship from Phase 2 onwards
- ▪Live projects, portfolio building and placement support
From cloud fundamentals to production MLOps.
A solid foundation in cloud computing and DevOps, plus the MLOps skills to move models from dev to production. Hands-on, real infrastructure, no fluff.
Cloud and infrastructure skills
- ▪AWS cloud computing fundamentals
- ▪Docker containerization
- ▪Kubernetes orchestration
- ▪Infrastructure as Code with Terraform
MLOps and deployment skills
- ▪CI/CD pipelines (Jenkins & GitHub Actions)
- ▪ML model deployment & monitoring
- ▪MLOps tools: MLflow & Kubeflow
- ▪Cloud security & cost optimization
The stack you’ll master.
The same industry-relevant tools used by cloud teams, DevOps engineers and MLOps specialists. You’ll get hands-on with every one.
Built for people who want to run real infrastructure.
No prior cloud experience needed. We start from fundamentals. Basic Linux knowledge helps but isn’t required.
Fresh graduates
Computer science or a related field, looking for an in-demand specialization.
Software developers
Ready to move into cloud and DevOps roles with real deployment skills.
Data scientists
Want to learn how to actually deploy and monitor your models in production.
System administrators
Upgrade to cloud-native skills and high-demand platform roles.
Learn by building things people pay for.
This track is built around projects that teach you by doing, and create a portfolio employers can review. Every project goes straight to your GitHub.
Deploy ML Model on AWS
Train a model locally, containerize it with Docker, and deploy on AWS EC2 with a REST API.
CI/CD Pipeline
Build a complete GitHub Actions pipeline that tests, builds, and deploys automatically.
Kubernetes Cluster Setup
Deploy a scalable web application on a Kubernetes cluster with auto-scaling.