Sokkary | Code3arabi

Shared Responsibility Model in the Cloud In this lesson, you’ll learn about the Shared Responsibility Model, which outlines the division of responsibilities between the cloud provider (e.g., Microsoft Azure) and the customer across different cloud service models (IaaS, PaaS, SaaS). 🔄 What is the Shared Responsibility Model? The Shared Responsibility Model defines which security tasks are handled by the cloud provider and which by the customer. The level of responsibility shifts depending on whether you are using IaaS, PaaS, or SaaS. 🔐 Responsibilities Breakdown Responsibility Cloud Provider Customer Physical security of datacenters ✔️ ❌ Network controls (firewalls, traffic filtering) Depends on model Depends on model Data classification and protection ❌ ✔️ User access management ❌ ✔️ ...

Describe Cloud Computing Models In this lesson, you’ll explore the three main cloud computing models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). 📦 The Three Cloud Models IaaS (Infrastructure as a Service): Provides virtualized computing resources such as virtual machines, storage, and networking. You manage the OS, apps, and data. Example: Azure Virtual Machines. PaaS (Platform as a Service): Offers a platform for building, testing, and deploying applications without managing the underlying infrastructure. Example: Azure App Service. SaaS (Software as a Service): Delivers fully functional applications over the internet. Users just use the software; the provider manages everything. Example: Microsoft 365, Outlook.com. 📊 Comparison Table Model You Manage Provider Manages IaaS OS, Middleware, Apps, Data Hardware, Networking ...

Describe the Benefits of Cloud Computing In this lesson, you’ll explore the main benefits that cloud computing offers to individuals and organizations, helping them to be more efficient, agile, and secure. 🌟 Key Benefits of Cloud Computing Cost Efficiency: Reduces capital expenses (CapEx) by eliminating the need for hardware purchases and maintenance. Scalability: Easily scale resources up or down to meet changing workloads. High Availability: Provides reliable access to services and data with built-in redundancy and failover support. Performance: Offers access to high-performance computing infrastructure and globally distributed data centers. Security: Includes built-in security features like encryption, access controls, and compliance certifications. Business Continuity: Supports data backup, disaster recovery, and geographic redundancy. ⚙️ Operational Advantages Agility: Quickly deploy and experiment with new ideas without long p...

Lesson 1.4: Benefits of Using AI on Azure In this lesson, you'll discover the main benefits of using Microsoft Azure for building and deploying AI solutions efficiently and responsibly. 🚀 Key Benefits Prebuilt AI Models: Access to ready-to-use Cognitive Services that accelerate AI development without requiring deep machine learning expertise. Customizability: Customize models using your own data with services like Custom Vision, Azure Machine Learning, and Language Understanding. Scalability: Run AI workloads across a global, enterprise-grade cloud infrastructure that supports high availability and autoscaling. Security and Compliance: Built-in support for identity, role-based access control, and compliance with global regulations (e.g., GDPR, ISO). 🔧 Development Efficiency Tooling Integration: Seamlessly integrates with tools like Visual Studio Code, Azure Machine Learning Studio, and Jupyter Notebooks. API and SDK Access: Easy ac...

Lesson 1.3: Introduction to Azure AI Services In this lesson, you'll explore the key AI services offered by Microsoft Azure and how they simplify building intelligent applications without needing deep data science expertise. 🧰 Azure AI Service Categories Azure Machine Learning: A cloud-based platform for building, training, and deploying machine learning models. Azure Cognitive Services: Pre-built APIs for vision, speech, language, and decision-making capabilities. Azure OpenAI Service: Provides access to powerful generative AI models such as GPT for text, image, and code generation. Azure Bot Services: Tools to create, test, and deploy conversational AI bots integrated with other Azure services. 🧠 Azure Cognitive Services Breakdown Vision: Analyze images, recognize objects, detect faces, and extract text using OCR. Speech: Convert speech to text, text to speech, and perform speech translation. Language: Perform sentiment analy...

Lesson 1.2: Types of AI Workloads In this lesson, you'll learn about the different types of AI workloads and how they are categorized based on the tasks they perform. 📦 Common AI Workloads Machine Learning: Training models on historical data to make predictions or classifications on new data. Anomaly Detection: Identifying unusual patterns that do not conform to expected behavior, often used in fraud detection or equipment monitoring. Computer Vision: Processing and analyzing visual data (images or video), such as recognizing objects or detecting faces. Natural Language Processing (NLP): Interpreting and generating human language, including speech and text. Conversational AI: Building bots and virtual assistants that understand and respond to user input in natural language. 🔁 Training vs Inference Training: The process of teaching an AI model using historical data and labels to adjust internal parameters. Inference: Using a tra...