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Azure MLOps Retail Forecast Platform

End-to-end MLOps pipeline với Azure Machine Learning & Azure Native Services

👨‍💻 Tác giả 1

Nguyễn Thanh Nhân

Cloud Engineer

👩‍💻 Tác giả 2

Hà Khả Nguyên

Data Engineer

👩‍💻 Tác giả 3

Phạm Thị Anh Đào

Data Analyst

👩‍💻 Tác giả 4

Dương Thị Quỳnh Giang

Data Analyst

Azure MLOps Technology Stack & Architecture

☁️ Azure Foundation
Resource Groups + Key Vault + RBAC
🤖 ML Platform
Azure ML + Model Registry + Compute
🐳 Containers
ACR + Managed Endpoints + AKS
💾 Data & Storage
Azure Storage + Data Assets + Versioning
🚀 CI/CD & DevOps
Azure DevOps + GitHub Actions
📊 Monitoring & Security
App Insights + Monitor + Activity Log

📚 Azure MLOps Workshop Steps

18 Task Azure MLOps Hoàn Chỉnh

End-to-end MLOps pipeline với Azure Machine Learning từ Resource provisioning đến Deployment và Monitoring

🏗️ Foundation
🤖 ML Training
🚀 Deployment
📊 Monitoring
🔄 CI/CD
⚡ Advanced

🏗️ Foundation & Registry

🤖 ML Training & Registry

🚀 Inference Deployment

📊 Monitoring & Observability

🔄 Advanced Features

🚀 Bắt đầu Azure MLOps Workshop

📋 Prerequisites
Azure Subscription, Azure CLI, Docker, Python
⏱️ Thời gian
18-22 giờ (Azure MLOps end-to-end)
📈 Level
Intermediate to Advanced
🎯 Bắt đầu với Task 1: Azure MLOps Architecture Overview

✨ Điểm nổi bật của Azure MLOps Workshop

☁️
Azure Native
100% Azure services integration
🤖
Azure ML Platform
Managed training & deployment
🚀
Managed Endpoints
Serverless inference scaling
🔒
Enterprise Security
RBAC + Key Vault + Managed Identity
🔄
Azure DevOps
End-to-end CI/CD pipelines
💰
Cost Optimized
Scale-to-zero + lifecycle policies
⬆️