Course Details

Courses Descriptions

☁️🤖 Cloud Computing with ML Ops - 9 Months Curriculum

🏁 From Beginner to Advanced | With Projects & Certifications

📌 Month 1: Cloud Fundamentals (AWS/GCP/Azure Basics)

• Introduction to Cloud Computing

• Understanding IaaS, PaaS, SaaS

• Core services: Compute, Storage, Networking

• Hands-on: AWS Free Tier / GCP Sandbox

• Project: Launch and manage a basic EC2/VM instance

• 🎓 Certification Prep: AWS Cloud Practitioner / Azure Fundamentals

📌 Month 2: Linux, Networking & Scripting Basics

• Linux command-line essentials

• File systems, permissions, process management

• Basics of Networking (IP, DNS, Load Balancer)

• Shell scripting (Bash)

• Project: Automate system tasks with shell scripts

📌 Month 3: DevOps Essentials

• Introduction to DevOps & CI/CD concepts

• Git, GitHub, and Version Control

• Jenkins / GitHub Actions basics

• Docker Introduction

• Project: Build a CI/CD pipeline for a sample app

• 🎓 Certification Prep: Docker Associate / DevOps Foundation

📌 Month 4: Containers & Kubernetes

• Deep dive into Docker: Images, Volumes, Networking

• Kubernetes basics: Pods, Services, Deployments

• Helm charts & K8s dashboard

• Minikube & Managed K8s (EKS, GKE, AKS)

• Project: Deploy a ML model as a containerized service on Kubernetes

📌 Month 5: Python for ML & Cloud Automation

• Python basics to intermediate

• File handling, API requests, Automation

• Pandas, NumPy for data processing

• Boto3 & GCP SDK for cloud automation

• Project: Automate cloud resource creation with Python

📌 Month 6: Machine Learning Foundations

• Data cleaning, Feature Engineering

• Regression, Classification models

• Model evaluation (Confusion Matrix, ROC-AUC)

• Project: Build a ML model for prediction task

• 🎓 Certification Prep: Google AI Essentials / AWS Machine Learning Foundational

📌 Month 7: ML Ops Pipeline - Model to Production

• Model packaging (Pickle, Joblib)

• CI/CD for ML models

• Model monitoring & logging

• MLflow / Kubeflow basics

• Project: Full ML Pipeline from training to deployment using MLflow

📌 Month 8: Cloud-native ML Ops Tools

• AWS SageMaker / Vertex AI / Azure ML Studio

• Model tuning & hyperparameter optimization

• AutoML tools overview

• Deploy REST APIs with Flask/FastAPI

• Project: Deploy ML API to cloud with auto-scaling

📌 Month 9: Capstone Projects & Job Prep

• 🎯 Capstone Project: Real-world ML Ops project (e.g., Customer churn, Demand Forecasting)

• Resume & Portfolio Building

• Mock Interviews & Career Guidance

• 🎓 Final Certifications: ML Ops Tools + Cloud Certification

• GitHub Portfolio, Project Presentation

🎁 Tools & Technologies Covered:

AWS, GCP, Azure, Docker, Kubernetes, Jenkins, Git, MLflow, Python, Flask, FastAPI, Pandas, NumPy, Scikit-learn, GitHub Actions, Terraform (Intro), Boto3

🏆 You Will Get:

• ✅ 5+ Industry Projects

• ✅ Dual Certification: Cloud + ML Ops

• ✅ Interview Preparation & Job Assistance

• ✅ Access to Live Sessions & LMS