/hol-azure-machine-learning

Introduction to Machine Learning and Azure Machine Learning Services. Hands on labs to show Azure Machine Learning features, developing experiments, feature engineering, R and Python Scripting, Production stage, publishing models as web service, RRS and BES usage

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Azure Machine Learning Hands on Labs

Suggested timeline for Azure Machine Learning Hands On Lab (HOL)

Time (min) Activity
50 Introduction to Machine Learning
20 Lab1 - Setting up development environment
45 Lab2 - Introduction to R, Python & Data Synth
45 Lab3 - AzureML Experiments & Data Interaction
60 Lab4 - Develop and Consume AzureML Models
45 Lab5 - Custom Scripts (R & Python) in AML
60 Lab6 - Evaluate model performance in AML
60 Lab7 - Azure ML Batch Score, Retrain, Production and Automatization

Detailed contents of the HOL

  1. Setting up development environment

    • Overview
      • Objectives
      • Requirements
    • Create free tier Azure ML account
    • Create standard tier Azure ML account
    • Install R and R Studio
    • Install Anaconda Python

  2. Introduction to R, Python & Data Synth

    • Overview
      • Objectives
      • Requirements
    • Generate Synthetic Data
      • Microsoft Excel
      • R
      • Python
      • Microsoft Azure SQL Server
      • Microsoft Azure Blob Storage
    • Other Dataset sources

  3. AzureML Experiments & Data Interaction

    • Overview
      • Objectives
      • Requirements
    • Creating AzureML Experiment
    • Accessing Data
      • Access data, use existing dataset
      • Upload your own dataset
      • Upload your own compressed dataset
      • Manually enter data
      • Access data on Azure Storage
      • Access data on Azure SQL Database

  4. Develop and Consume AzureML Models

    • Overview
      • Objectives
      • Requirements
    • Working with AzureML Models
      • Training a model
      • Publishing a trained model as Web Service
      • Removing Web Service Redundant input & output parameters
      • Consume the ML Web Service in a C# application
      • Input data type

  5. Custom Scripts (R & Python) in AML

    • Overview
      • Objectives
      • Requirements
    • R & Python Script Modules
      • Using Execute R Script module
      • Using Python Script module
      • R & Python compatibility with Azure ML

  6. Evaluate model performance in AML

    • Overview
      • Objectives
      • Requirements
    • Performance evaluation
      • Splitting data
      • Scoring the model
      • Evaluate a Regression model
      • Evaluate more than one model
      • Cross Validation
    • Performance evaluation (cont.)
      • Evaluate a Binary classification model
      • Comparing two binary classification model
      • Cross Validation on Binary Classification
      • Evaluating a Multi-class classification model
    • Feature engineering
      • Which feature is or is not important?
      • Simpler method to measure a feature’s importance

  7. Azure ML Batch Score, Retrain, Production and Automatization

    • Overview
      • Objectives
      • Requirements
    • Importance of Retraining, seeing the whole picture
    • Batch and Request/Response scoring web services
      • Stages to create a scoring web service
        • Request/Response Service (RRS)
        • Batch Execution Service (BES)
        • Web Service Input/Output Parameter alternatives
    • Azure ML Retraining