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.

🧠 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

⚙️ 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

🧪 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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