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Advanced Cloud Computing – Detailed Combined Guide

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Unit 1: Introduction to Networking in the Cloud

Networking Basics

• LAN (Local Area Network): Network within a physical location like a building, office, or home.
• WAN (Wide Area Network): Connects LANs over geographical distances (internet, data centers).

Virtual Private Cloud (VPC)

• VPC is a private network hosted inside a public cloud environment.
• Allows users to run private cloud operations such as storing data, hosting apps, etc.

Subnets and IP Addressing

• Subnets divide networks into logical segments:
  • Example subnets defined with CIDR notations like 10.240.0.0/24, 192.168.1.0/24.
• IP Addresses:
  • External/Public: Routable by the internet.
  • Internal/Private: Not routable, used within clouds or private LANs.
• IPs assigned dynamically within subnets and regions allow VM instances and resources communication.

Network Firewall Rules

• VPC has firewall rules controlling inbound/outbound traffic per project and network.
• Characteristics:
  • Stateful: Allows return traffic matching initiated connections.
  • Supports IPv4 and IPv6.
  • Rules applied per direction (ingress or egress).
• Multiple, segregated networks may coexist with specific firewall rules for security.

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Unit 2: Cloud Networking Platforms & Google Cloud

Google App Engine (GAE)

• Platform-as-Service (PaaS) for building scalable web applications.
• Needs apps in Java or Python, storing data in Google Bigtable.
• Features:
  • Blobstore for large files.
  • Cloud Storage.
  • URL Fetch for HTTP requests.
  • Memcache for in-memory caching.

Google Cloud Console & Cloud Shell

• Web-based Azure interface to manage GCP resources.
• Cloud Shell provides command-line tools in a browser VM with persistent storage.

Regions & Zones

• Resources deployed in regional zones for high availability.
• Commands available to set defaults via gcloud CLI.

Deploying Compute Instances

• Use startup scripts and automation to provision instances (e.g., Node.js app example).
• Canary or rolling updates possible to limit downtime.

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Unit 3: Microsoft Cloud Services – Microsoft Azure

Overview

• Cloud platform offering IaaS, PaaS, and SaaS.
• Known for scalability, reliability, hybrid multi-cloud support.
• Focus on AI, IoT, machine learning, and security.

Core Azure Services

• Compute: Virtual Machines, Containers (AKS), Serverless (Functions).
• Storage: Blob, Queue, Table storage.
• Networking: Virtual Networks (VNets), firewalls, Load Balancers.
• Databases: SQL Database, Cosmos DB (multi-model NoSQL), MySQL, PostgreSQL.
• AI & ML: Cognitive Services, Machine Learning Studio, Bot Service.
• Security: Azure Security Center, Defender, Sentinel, IAM.
• Management Tools: Azure Monitor, Resource Manager, Automation, Cost Management.

Hybrid and Multi-cloud Support

• Allows seamless integration between on-premises, public, and multiple clouds.
• Critical for enterprise agility and legacy system integration.

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Unit 4: Amazon Cloud Services – AWS

Global Cloud Architecture

• Data centers spread globally.
• Regions and Availability Zones for redundancy and latency.

Core AWS Services

• Compute: EC2 (virutal servers), Lambda (serverless).
• Storage: S3 (object storage), Elastic Block Store (EBS), Glacier (archival).
• Databases: RDS (SQL), DynamoDB (NoSQL), Aurora, Redshift (Data Warehouse).
• Networking: VPC (Virtual Private Cloud), Route 53 (DNS), CloudFront (CDN).
• Management: AWS Management Console, CLI, CloudFormation (Infrastructure as Code).

Pricing & Benefits

• Pay-as-you-go.
• Spot Instances for cost savings.
• Reserved Instances for long-term discounts.

Web App Hosting on AWS

• S3 for static sites.
• EC2 for dynamic web apps.
• Elastic Beanstalk for managed app hosting.
• AWS Amplify for frontend-backend app development.

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Unit 5: Cloud Machine Learning and AI

Machine Learning Basics

• ML: Algorithms optimize models based on data.
• Types: Supervised, Unsupervised, Reinforcement learning.
• Models can be predictive or descriptive.

Building ML Models on Cloud

• Data preparation and pipelines.
• Use Python SDKs like Vertex AI for development.
• Train models locally or distributed.
• Automate training, evaluation, deployment using pipelines.

Model Deployment and Monitoring

• Choose appropriate hardware (CPU/GPU/TPU).
• Monitor for data drift, skew.
• Fine-tune alert thresholds.
• Usage of Vertex AI Feature Store for feature management.

Google’s Pre-Trained APIs and AutoML

• Vision API for image recognition.
• Cloud Speech for speech to text.
• Natural Language API for text analytics.
• Cloud AutoML services allow business users to train custom models without deep ML expertise.

ML Workflow in GCP

• Prepare data, experiment, train, evaluate, deploy.
• Continuous monitoring and improvements.

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