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====== 30 Day Plan to Deploy AI in Azure ======

Here's a **30-day learning and preparation plan** to get ready to **deploy and support Azure AI solutions**, assuming you're working **full-time (5 days/week, 8 hours/day)**. This plan is focused on practical skills, foundational knowledge, and hands-on experience across **Azure AI services** like Azure OpenAI, Azure Machine Learning, Cognitive Services, and Bot Services.

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**🧠 Week 1: Azure Fundamentals & AI Overview**

**Goal:** Build foundational Azure and AI service knowledge.

**Day 1–2: Azure Core Concepts**

  * Learn:
    * Azure architecture, subscriptions, resource groups, regions
    * Azure Portal, CLI, Resource Manager (ARM templates)
  * Hands-on:
    * Set up Azure subscription and sandbox
    * Deploy basic resources (e.g., Storage, VMs)
  * Resources:
    * Microsoft Learn: Azure Fundamentals

**Day 3–4: Introduction to Azure AI Services**

  * Learn:
    * Overview of Azure AI stack: Azure Machine Learning, Azure OpenAI, Cognitive Services, AI Search
    * Use cases and architecture patterns
  * Hands-on:
    * Explore Azure AI Studio
    * Try out basic pre-built models (e.g., language, vision)
  * Resources:
    * Azure AI documentation
    * Azure AI Fundamentals (AI-900)

**Day 5: Basic Governance & Security**

  * Learn:
    * Role-Based Access Control (RBAC), managed identities
    * Azure Key Vault and AI service permissions
  * Hands-on:
    * Configure access and secrets for an AI service

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**⚙️ Week 2: Azure OpenAI & Cognitive Services**

**Goal:** Deploy and manage foundational AI APIs and large language models.

**Day 6–7: Azure OpenAI Service**

  * Learn:
    * Models: GPT, Embeddings, Prompts, Token limits
    * Responsible AI & content filtering
  * Hands-on:
    * Deploy Azure OpenAI resource
    * Use GPT-4 in a simple chat app or data Q&A
  * Resources:
    * Azure OpenAI Service Documentation
    * Prompt Engineering Guide (e.g., OpenAI Cookbook)

**Day 8–9: Azure Cognitive Services**

  * Learn:
    * Key APIs: Vision, Speech, Language, Translator
    * Use case integration (e.g., OCR, sentiment analysis)
  * Hands-on:
    * Create and test multiple cognitive service APIs in the Azure Portal and with Python/REST
  * Resources:
    * Cognitive Services Overview

**Day 10: Monitoring and Cost Management**

  * Learn:
    * Azure Monitor, Metrics, Logs
    * Cost management tools and quotas
  * Hands-on:
    * Set up alerts, budget thresholds, and diagnostic logging

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**🧪 Week 3: Azure Machine Learning**

**Goal:** Deploy ML workflows and models using Azure Machine Learning.

**Day 11–12: Azure Machine Learning Basics**

  * Learn:
    * Workspaces, compute targets, environments
    * Notebooks, data assets, model registration
  * Hands-on:
    * Build and deploy a basic ML model using AutoML or a notebook
  * Resources:
    * Azure ML Fundamentals

**Day 13–14: Pipelines and Responsible AI**

  * Learn:
    * ML pipelines, scheduling, and versioning
    * Responsible AI dashboard, model explainability
  * Hands-on:
    * Create and run an ML pipeline
    * Review fairness and explainability metrics

**Day 15: Real-World Deployment**

  * Learn:
    * Real-time vs batch inference
    * Endpoint management and scaling
  * Hands-on:
    * Deploy and consume a model endpoint from a web app or script

----

**🤖 Week 4: Bots, Integration, and Final Practice**

**Goal:** Prepare for real-world deployment, integration, and troubleshooting.

**Day 16–17: Bot Framework & AI Integration**

  * Learn:
    * Azure Bot Service, Bot Framework Composer
    * Integration with LLMs and Cognitive Services
  * Hands-on:
    * Build and deploy a chatbot integrated with Azure OpenAI

**Day 18–19: Enterprise Readiness & CI/CD**

  * Learn:
    * Infrastructure-as-code (Bicep, ARM, Terraform)
    * GitHub Actions / Azure DevOps for model deployment
  * Hands-on:
    * Build a simple CI/CD pipeline for an ML model or bot

**Day 20: Review & Mock Deployment**

  * Task:
    * Design and deploy a full-stack demo AI solution (e.g., document Q&A bot, image analyzer)
    * Include monitoring, secure endpoints, and user access

----

**📘 Bonus: Certification & Continuing Education**

  * **Optional**: Prepare for **AI-102** (Designing and Implementing Azure AI Solutions) if you're aiming to validate your skills.
  * Ongoing learning via:
    * Microsoft Learn
    * GitHub examples and the OpenAI Cookbook
    * Azure AI YouTube channels and blogs

**🗂️ Trello Board Structure: "Azure AI 30-Day Learning Plan"**

**🧭 List: Week 1 – Azure & AI Fundamentals**

**Cards:**

  * ✅ Set up Azure subscription and sandbox environment
  * 🧠 Learn Azure core services (VMs, Storage, Networking, ARM)
  * 🛠️ Practice deploying resources via Portal and CLI
  * 🔍 Learn Azure AI services: OpenAI, Cognitive Services, Azure ML
  * 🧪 Test prebuilt models in Azure AI Studio
  * 🔐 Learn RBAC, Managed Identities, and Azure Key Vault
  * 🔧 Set up Key Vault secrets for AI services

----

**🧠 List: Week 2 – OpenAI & Cognitive Services**

**Cards:**

  * 📚 Learn Azure OpenAI: GPT, models, use cases, limits
  * ⚙️ Deploy Azure OpenAI resource and test with prompt playground
  * 💬 Build a simple chat demo using OpenAI completion/chat API
  * 🔍 Learn Cognitive Services: Language, Vision, Speech, Translator
  * 🧪 Build an app using at least 2 Cognitive Service APIs
  * 📊 Explore monitoring: Logs, metrics, diagnostics for AI resources
  * 💵 Set up budgets and quotas in Cost Management

----

**⚙️ List: Week 3 – Azure Machine Learning**

**Cards:**

  * 🧠 Learn Azure ML: Workspaces, Compute, Notebooks, Environments
  * ⚙️ Create a dataset and run an AutoML training job
  * 🧪 Train and register a model manually via notebook
  * 🔁 Learn ML pipelines: Build and run a simple pipeline
  * 🌐 Deploy a model as a real-time endpoint
  * 🔍 Review fairness/explainability using Responsible AI dashboard
  * 🧩 Test consuming your endpoint from a REST client or app

----

**🤖 List: Week 4 – Bots, Integration & End-to-End Deployment**

**Cards:**

  * 🤖 Learn Azure Bot Service + Bot Framework Composer
  * 🔧 Build and deploy a bot integrated with OpenAI
  * 🛠️ Learn Infrastructure-as-Code with Bicep or Terraform
  * 🔁 Create a CI/CD pipeline for ML model or bot using GitHub Actions
  * 📈 Add logging, alerts, and diagnostics to your solution
  * 🧪 Perform a mock deployment of a full AI solution (e.g., Q&A bot)
  * 🔄 Review architecture for scalability and security

----

**🏁 List: Done ✅**

  * Move cards here once completed

----

**📌 Extra Lists (Optional):**

**📖 Resources**

  * Microsoft Learn: Azure AI
  * Azure AI Studio: https://ai.azure.com
  * Azure OpenAI: https://learn.microsoft.com/en-us/azure/ai-services/openai/
  * Azure ML Notebooks: https://github.com/Azure/MachineLearningNotebooks

**🧩 Certification Prep**

  * AI-900: Azure AI Fundamentals
  * AI-102: Designing and Implementing Azure AI Solutions

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