AI News, Provision a Linux CentOS Data Science Virtual Machine on Azure

Provision a Linux CentOS Data Science Virtual Machine on Azure

The Linux Data Science Virtual Machine is a CentOS-based Azure virtual machine that comes with a collection of pre-installed tools.

The key software components included are: Doing data science involves iterating on a sequence of tasks: Data scientists use various tools to complete these tasks.

It can be quite time consuming to find the appropriate versions of the software, and then to download, compile, and install these versions.

You pay only the Azure hardware usage fees that are assessed based on the size of the virtual machine that you provision with the VM image.

To connect to the Linux VM graphical desktop, do the following on your client: After you sign in to the VM by using either the SSH client or XFCE graphical desktop through the X2Go client, you are ready to start using the tools that are installed and configured on the VM.

If you are using the Emacs editor, note that the Emacs package ESS (Emacs Speaks Statistics), which simplifies working with R files within the Emacs editor, has been pre-installed.

This distribution contains the base Python along with about 300 of the most popular math, engineering, and data analytics packages.

Since we have both Python 2.7 and 3.5, you need to specifically activate the desired Python version (conda environment) you want to work on in the current session.

To activate the Python 2.7 conda environment, run the following command from the shell: Python 2.7 is installed at /anaconda/bin.

To install additional Python libraries, you need to run conda or pip command under sudo and provide full path of the Python package manager (conda or pip) to install to the correct Python environment.

You can see the link to the samples on the notebook home page after you authenticate to the Jupyter notebook by using your local Linux user name and password.

standalone instance of Apache Spark is preinstalled on the Linux DSVM to help you develop Spark applications locally first before testing and deploying on large clusters.

Before running in Spark context in Microsoft R Server, you need to do a one time setup step to enable a local single node Hadoop HDFS and Yarn instance.

In order to enable it, you need to run the following commands as root the first time: You can stop the Hadoop related services when you dont need them by running systemctl stop hadoop-namenode hadoop-datanode hadoop-yarn A

sample demonstrating how to develop and test MRS in remote Spark context (which is the standalone Spark instance on the DSVM) is provided and available in the /dsvm/samples/MRS directory.

It allows you to create, develop, test, and deploy Azure applications using the Eclipse development environment that supports languages like Java.

The open source database Postgres is available on the VM, with the services running and initdb already completed.

The ODBC driver package for SQL Server also comes with two command-line tools: bcp: The bcp utility bulk copies data between an instance of Microsoft SQL Server and a data file in a user-specified format.

The bcp utility can be used to import large numbers of new rows into SQL Server tables, or to export data out of tables into data files.

To import data into a table, you must either use a format file created for that table, or understand the structure of the table and the types of data that are valid for its columns.

sqlcmd: You can enter Transact-SQL statements with the sqlcmd utility, as well as system procedures, and script files at the command prompt.

Azure Machine Learning is a fully managed cloud service that enables you to build, deploy, and share predictive analytics solutions.

Vowpal Wabbit is a machine learning system that uses techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

The objective of this library is to push the computation limits of machines to the extremes needed to provide large-scale tree boosting that is scalable, portable, and accurate.

Here is a simple example you can run in R prompt: To run the xgboost command line, here are the commands to execute in the shell: A

It presents statistical and visual summaries of data, transforms data that can be readily modeled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new data sets.

It also generates R code, replicating the operations in the UI that can be run directly in R or used as a starting point for further analysis.

In some of the steps below, you are prompted to automatically install and load some required R packages that are not already on the system.

Especially for beginners in R, this is an easy way to quickly do analysis and machine learning in a simple graphical interface, while automatically generating code in R to modify and/or learn.

Getting Started with Microsoft Azure Machine Learning

Machine Learning A-Z™: Hands-On Python & R In Data Science ☞ Deep Learning A-Z™: Hands-On Artificial Neural ..

Introduction to R with Azure Machine Learning

Tutorial video on using R in the Microsoft Azure Machine Learning environment. This video complements the Quick Start Guide to R in Azure ML at ...

Microsoft Azure Machine Learning Tutorial

Machine Learning A-Z™: Hands-On Python & R In Data Science ☞ Deep Learning A-Z™: Hands-On Artificial Neural ..

Intro to Azure ML: What is Azure Machine Learning?

What's better than machine learning? Machine learning where coding is optional! Drag and drop machine learning with a visual interface! We're going to ...

Using R in Azure Machine Learning

Data Visualization in R using ggplot lessons "Learn and Understand AngularJS tutorials" : complete ios9 .

Data transformations and time series modeling with R and Azure ML

This tutorial video illustrates how to perform some basic data transformations and time series modeling using R and Microsoft's Azure Machine Learning.

The Best Way to Prepare a Dataset Easily

In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it).

Solving the Titanic Kaggle Competition in Azure ML

In this tutorial we will show you how to complete the titanic Kaggle competition using Microsoft Azure Machine Learning Studio.This video assumes you have an ...

Deploy R Model as a Web Service on Azure ML

For all you R programmers, it's now possible to build models in R and deploy it on Azure Machine Learning studio as a Web Service. Please follow this vlog and ...

Azure Machine Learning Studio: Summarize data, normalize data, clean missing data

Dataset: Next video: ..