Azure Machine Learning
  • Table of Contents
  • Cover
  • Dedication
  • About the Author
  • Section 1: Introduction
    • The Machine Learning Process and Azure ML
    • Introduction to Azure Machine Learning Service
    • Creating and Managing Pipelines
  • Section 2: Data Acquisition and Preparation
    • Ingesting Data
    • Cleaning and Visualizing Data
  • Section 3: Model Training, Testing, and Evaluation
    • Training and Testing the Data
    • Interpreting and Explaining the Model
    • Automating Machine Learning
  • Section 4: Model Maintenance
    • Deploying and Consuming the Model
    • Track and Monitor Experiments
    • Monitor Data and Models
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  • Evaluating a Technology
  • The Platform
  • The Process
  • The People
  1. Section 1: Introduction

Introduction to Azure Machine Learning Service

PreviousThe Machine Learning Process and Azure MLNextCreating and Managing Pipelines

Last updated 5 years ago

Evaluating a Technology

Any tool or technology to be used by an organization must undergo an in-depth analysis and evaluation of how it would address enterprise requirements and the effects it would have on existing organizational IT systems, controls, and governance. This is discussed below (refer to Figure 1-1).

The three P's outlined in Figure 1-1 are:

  1. People

  2. Process

  3. Platforms

In Chapter 0, we looked machine learning in general using the three P's; the Process or workflow of a machine learning solution, the People or roles that interact with the workflow, and the Platforms or tools/technologies/hardware for developing using that machine learning workflow. We will begin the discussion by introducing the platform, the impact it can possibly have on the process, and finally the people it affects.

The Platform

The Azure Machine Learning Service is a cloud-based service, available only in Microsoft Azure.

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The Process

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The People

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Figure 1-1: Tool/Technology within an Enterprise Landscape