Cloud & DevOps Development

The Cloud & DevOps for AI course is a 3-month hands-on program covering AWS, Azure, Google Cloud, Docker, Kubernetes, CI/CD pipelines, and MLOps. Learn to deploy, monitor, and scale AI models in production environments with industry-standard DevOps practices used at top tech companies.

Aevoriq Academy � Course

Cloud & DevOps for AI

A 3-month hands-on program covering AWS, Azure, Docker, Kubernetes, CI/CD pipelines, and MLOps. Learn to deploy, monitor, and scale AI applications in production environments using the exact tools top tech companies rely on.

Enroll Now 3 Months  •  Live + Recorded  •  Certificate
3
Months Duration
150+
Hours of Training
8+
Live Projects
Online
Learning Mode

What You Will Learn

Course Curriculum

Cloud Fundamentals (AWS & Azure)

Understand core cloud services � compute, storage, networking, IAM, pricing, and cloud architecture best practices.

Docker & Containerisation

Build, run, and manage Docker containers. Write Dockerfiles, use Docker Compose, and manage container registries.

Kubernetes Orchestration

Deploy and scale containerised applications on Kubernetes. Learn pods, services, deployments, Helm charts, and autoscaling.

CI/CD Pipelines

Automate testing and deployment with GitHub Actions, Jenkins, and Azure DevOps. Build robust, zero-downtime pipelines.

MLOps � AI in Production

Deploy ML models with FastAPI, monitor drift, retrain pipelines, and manage model versions with MLflow and DVC.

Infrastructure as Code

Provision cloud resources automatically using Terraform and Ansible. Manage environments consistently and repeatably.

Modules

Course Breakdown

01
Cloud Foundations

AWS EC2, S3, RDS, VPC, IAM, Azure equivalents, cloud cost management, security basics

02
Linux & Shell Scripting

Linux command line, bash scripting, file permissions, process management, cron jobs

03
Docker & Containerisation

Docker images, containers, volumes, networks, Docker Compose, container registry

04
Kubernetes Orchestration

Pods, deployments, services, ingress, ConfigMaps, Helm, horizontal pod autoscaling

05
CI/CD Automation

GitHub Actions, Jenkins pipelines, automated testing, deployment strategies, rollbacks

06
Infrastructure as Code

Terraform modules, state management, Ansible playbooks, environment provisioning

07
MLOps & Model Serving

FastAPI model APIs, MLflow experiment tracking, DVC data versioning, model monitoring

08
Capstone & Placement

Deploy a full ML system end-to-end, portfolio, mock interviews, hiring connect

Tech Stack

Tools & Technologies

AWSAzureDockerKubernetesHelmTerraformAnsibleGitHub ActionsJenkinsMLflowFastAPILinux

Master Cloud & DevOps for the AI Era

Every AI team needs a DevOps engineer. Get certified and job-ready in 3 months.

Enroll Now