cheneyszp / 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

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

• Introduction to Machine Learning

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