Getting Started With Azure Machine Learning Studio And Azure Storage

Introduction

This write-up is in continuation of my previous one and here, we will be sketching how to get started with Azure Machine Learning and get an introduction to Azure Storage.

Key points of this article, in short

• Kickstart Azure Machine Learning.
• Overview of Azure Storage.
• Creating an experiment in Azure ML Studio.

Get started with Azure Machine Learning Studio

Follow the below steps to get started with Azure Machine Learning Studio.

Requirements

1. Microsoft account – Outlook or Hotmail email account.

Try signing up from http://studio.azureml.net/ for Azure Machine Learning Studio. Once signed-in, you will land on the following page.



The home page of the Machine Learning Studio has several menus and options on it.

“Microsoft Azure Machine Learning Studio” will help you to take forward the browser for the homepage;
“Projects” deals with the collection of experiments, datasets, notebooks and other resources representing a single project;
“Experiments” helps in experimenting the project that we have created,
“Web Services” helps in deploying the experiments as web services namely web apps,
“Notebooks” helps in moving with Jupyter notebooks,
“Datasets” deals with the datasets that we have uploaded in Azure Machine Learning Studio,
“Trainer Models” helps in training the models and save the experiments on the Azure ML Studio,
“Settings” goes with a collection of settings that we can use to configure our account.

Experiment

An experiment normally holds at least one dataset which provides the data for analytical modules to help us to connect together and construct a predictive model. An experiment will be having the datasets connected to modules with workflows and required parameters for each module. There are a lot many experiments available in Azure ML Studio.

Datasets

Datasets are used in the modeling process. We have many datasets available in Azure ML Studio. Datasets can be of any kind, like calculations of diagnostics data for cancer, credit card fraud transaction details, etc.

Modules

Modules are algorithms that can perform on the data provided. This holds training, scoring, and validation process. After completing all these things, we can deploy the Azure ML experiment as a web service.

Brief introduction of Azure Storage

Azure Storage is of high availability, multi-tenant service designed for flexible storage options and it consists of four services - blobs, files, tables, and queues. Azure Storage supports scalability, durability, high availability, and cost-effectiveness. Azure Storage is multi-tenant which refers to an architecture in which a single instance of software runs on a server and serves for multiple tenants (a group of users who share the same resources).

Each storage over here has its own HTTP or HTTPS endpoint. For example, a storage account created as najumamahamuth will be as follows:

• Blob – https://najumamahamuth.blob.core.windows.net
• Table – https://najumamahamuth.table.core.windows.net
• Queue – https://najumamahamuth.queue.core.windows.net
• Files - https://najumamahamuth.files.core.windows.net

This storage service can support up to 500 TB of capacity with 20,000 IOPS and up to 20 Gbps in and 30 out for LRS - up to 10Gbps in and 20 out for GRS - and helps in security through management certificate, RBAC, or name and authentication key.