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)

  📖 Learning Outcomes:

  • Understand Cloud Computing concepts & architecture

  • Master IaaS, PaaS, SaaS service models

  • Learn about compute (VMs, EC2), storage (S3, Blob, GCS), and networking basics

  • Hands-on with AWS Free Tier / GCP Sandbox / Azure Free Account

  🛠 Hands-on Project:

  • Launch and manage a Virtual Machine (VM)

  • Host a simple web server (Apache/NGINX) on AWS/GCP

  🎓 Certifications Prepared:

  • AWS Cloud Practitioner

  • Microsoft Azure Fundamentals

  ✨ Career Impact: Builds the core cloud foundation needed for DevOps and ML Ops.

  📌 Month 2: Linux, Networking & Scripting Basics

  📖 Learning Outcomes:

  • Linux command-line mastery (navigating, managing files, permissions)

  • Process Management, Services, and System Monitoring

  • Networking Fundamentals: IP, DNS, Load Balancer, Firewalls, Ports

  • Introduction to Shell Scripting (Bash) for automation

  🛠 Hands-on Project:

  • Create shell scripts for:

    • Automated log backups

    • User management (create/delete users)

    • Simple network monitoring script

  ✨ Career Impact: Gain system admin & networking skills, crucial for Cloud/DevOps roles.

  📌 Month 3: DevOps Essentials

  📖 Learning Outcomes:

  • Fundamentals of DevOps culture, Agile, and CI/CD pipelines

  • Git & GitHub (branches, merges, pull requests, version control best practices)

  • CI/CD Automation with Jenkins & GitHub Actions

  • Introduction to Docker containers

  🛠 Hands-on Project:

  • Build a CI/CD pipeline for a sample app (auto build → test → deploy)

  • Containerize a sample Python/NodeJS application using Docker

  🎓 Certifications Prepared:

  • Docker Associate

  • DevOps Foundation

  ✨ Career Impact: Gain skills to automate deployments — highly demanded by companies.

  📌 Month 4: Containers & Kubernetes (K8s)

  📖 Learning Outcomes:

  • Docker Advanced: Images, Volumes, Networking, Docker Compose

  • Kubernetes Fundamentals: Pods, Services, Deployments, Namespaces

  • Tools: Helm Charts, K8s Dashboard

  • Deploy locally with Minikube, scale with EKS, GKE, AKS

  🛠 Hands-on Project:

  • Deploy a Machine Learning model as a containerized service

  • Scale it on Kubernetes with multiple replicas

  ✨ Career Impact: Be industry-ready for DevOps Engineer roles (Docker + K8s skills).

  📌 Month 5: Python for Cloud & Automation

  📖 Learning Outcomes:

  • Python basics → intermediate: loops, functions, OOP, modules

  • File handling, REST API requests, JSON handling

  • Data manipulation with Pandas & NumPy

  • Cloud SDKs: AWS Boto3, GCP SDK for automation

  🛠 Hands-on Project:

  • Write Python scripts to:

    • Create an EC2 instance automatically

    • Upload files to S3 bucket

    • Monitor cloud resources

  ✨ Career Impact: Become capable of cloud automation & scripting, a must for ML Ops.

  📌 Month 6: Machine Learning Foundations

  📖 Learning Outcomes:

  • Data Preprocessing & Cleaning (handling missing data, encoding)

  • Feature Engineering for ML models

  • Algorithms: Regression, Classification, Decision Trees, Random Forest

  • Evaluation Metrics: Accuracy, Confusion Matrix, ROC-AUC, Precision-Recall

  🛠 Hands-on Project:

  • Build a prediction ML model (e.g., housing prices, churn analysis, fraud detection)

  • Evaluate performance with industry metrics

  🎓 Certifications Prepared:

  • Google AI Essentials

  • AWS Machine Learning Foundational

  ✨ Career Impact: Step into the Data Science + ML world with practical skills.

  📌 Month 7: ML Ops Pipeline – Model to Production

  📖 Learning Outcomes:

  • Packaging models using Pickle, Joblib, ONNX

  • CI/CD for ML models

  • Monitoring & logging ML pipelines

  • Tools: MLflow, Kubeflow basics

  🛠 Hands-on Project:

  • Develop a complete ML Ops pipeline:

    • Train ML model → Containerize → Deploy → Monitor

  ✨ Career Impact: Bridge the gap between ML research and production deployment.

  📌 Month 8: Cloud-native ML Ops Tools

  📖 Learning Outcomes:

  • Managed ML Services: AWS SageMaker, GCP Vertex AI, Azure ML Studio

  • Model tuning, hyperparameter optimization

  • Introduction to AutoML tools

  • Deploying APIs with Flask / FastAPI

  • Auto-scaling ML apps on cloud

  🛠 Hands-on Project:

  • Deploy a Flask/FastAPI ML model API on AWS/GCP with auto-scaling enabled

  ✨ Career Impact: Get skilled in enterprise ML Ops tools used by top companies.

  📌 Month 9: Capstone Project & Career Preparation

  📖 Capstone Project (Choose One):

  • Customer Churn Prediction with automated cloud deployment

  • Demand Forecasting for e-commerce using ML Ops pipeline

  • Fraud Detection System with model monitoring

  📖 Career Support:

  • Resume & LinkedIn Building (ATS-friendly)

  • GitHub Portfolio Setup (all projects uploaded)

  • Mock Interviews & Career Guidance with experts

  🎓 Final Certifications:

  • Cloud (AWS/GCP/Azure) + ML Ops

  • Showcase GitHub Portfolio + Final Project Presentation

  ✨ Career Impact: Become job-ready with a portfolio, certifications & real projects.

  🎁 Tools & Technologies You Will Master

  🌐 Cloud: AWS, GCP, Azure
  ⚙️ DevOps: Docker, Kubernetes, Jenkins, GitHub Actions, Terraform (Intro)
  🐍 Programming & ML: Python, Pandas, NumPy, Scikit-learn, MLflow
  🖥 Web & APIs: Flask, FastAPI
  🤖 Automation: Boto3, GCP SDK

  🏆 What You Will Get

  ✅ 5+ Industry-grade Projects to showcase on GitHub
  ✅ Dual Certification: Cloud + ML Ops
  ✅ Interview Preparation & Job Assistance
  ✅ Live Sessions + LMS Access + Doubt Support
  ✅ Capstone Project + Portfolio for Career Boost