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🤖 ML Pipeline Integration Tutorial

Level Duration

Integrate Azure Machine Learning with Synapse Analytics for end-to-end ML workflows. Learn feature engineering, model training, deployment, and monitoring.

🎯 Learning Objectives

  • ✅ Connect Synapse to Azure ML workspace
  • ✅ Build feature engineering pipelines with Spark
  • ✅ Train models at scale using Azure ML
  • ✅ Deploy models for batch and real-time scoring
  • ✅ Monitor ML pipelines and model performance

📋 Prerequisites

  • Azure ML workspace setup
  • Understanding of ML workflows
  • Completed Advanced Analytics Lab
  • Python and ML framework knowledge

🔗 Integration Architecture

┌─────────────────────────────────────────────────────────┐
│                  Azure Synapse Analytics                │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐ │
│  │ Data Lake    │  │ Spark Pool   │  │ SQL Pool     │ │
│  │ (Features)   │→ │ (Engineering)│→ │ (Serving)    │ │
│  └──────────────┘  └──────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│              Azure Machine Learning                     │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐ │
│  │ Experiments  │  │ Model Train  │  │ Model Deploy │ │
│  │ (MLflow)     │→ │ (Compute)    │→ │ (Endpoints)  │ │
│  └──────────────┘  └──────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────┘

🚀 Implementation Guide

[Content covering Synapse-AML integration setup, feature engineering with Spark, model training workflows, deployment strategies, batch/real-time scoring, and MLOps patterns]

Last Updated: January 2025