Oracle's purchase of NetSuite

When I read the news couple of days ago, I was interested in knowing why Oracle wants to buy NetSuite. Of course I guess there may be other reasons for this purchase.

I was interested in learning what unique features of NetSuite would make it attractive for a buyer. I just came across this site , http://gurussolutions.com/en/cloud-products/netsuite > which seems to answer this question.

Guru's site described the features why NetSuite would be desirable:

Efficient
Integrated
Visibility with good dashboards etc
Browser and mobile accessible from anywhere
Global
Adaptable and
Up to date with automatic upgrades with latest innovations

I did get the feeling that the same list things has been offered by other cloud vendors as well for quite some time. It just appeared to me like any other cloud vendor. I could be wrong, and perhaps I did not hit the right site to get answers.

Read more about NetSuite vis-a-vis Oracle buy

https://www.trustradius.com/products/netsuite/reviews

http://fortune.com/2016/09/07/t-rowe-price-vote-against-oracle-netsuite-deal/

http://www.marketwatch.com/story/oracle-earnings-clouded-by-netsuite-acquisition-uncertainty-2016-09-14


Video showing why BM Online chose NetSuite:

Azure ML: Predictive analytics as a Service (PaaaS?)

It is a fully managed Machine Learning Cloud service for predictive analytics solutions. It takes in historical data and create a statistics based model to predict future trends. It would immediately find applications in eCommerce (purchasing trends), pharmaceuticals, traffic; epidemics studies, etc. and forecast future events and take proactive steps. You can start building a service now.

Azure ML is,

• Designed for new and experienced users
• Proven algorithms from MS Research, Xbox and Bing
• First class support for the open source language R
• Seamless connection to HDInsight for big data solutions
• Deploy models to production in minutes
• Pay only for what you use. No hardware or software to buy.

Using Azure ML creating such a model makes it fast to predict and forecast. This has been done in the past but, the steep learning curve needed; the availability of knowledgeable staff with advanced model building capabilities, time to build the solution and resources required limited Machine learning to only large enterprises. What took weeks and months takes a few hours- according to Microsoft.

In Microsoft's own words this is what you can get from Azure ML,

"Azure ML, which previews next month (July), will bring together the capabilities of new analytics tools, powerful algorithms developed for Microsoft products like Xbox and Bing, and years of machine learning experience into one simple and easy-to-use cloud service. For customers, this means virtually none of the startup costs associated with authoring, developing and scaling machine learning solutions. Visual workflows and startup templates will make common machine learning tasks simple and easy. And the ability to publish APIs and Web services in minutes and collaborate with others will quickly turn analytic assets into enterprise-grade production cloud services."

Get to read everything about Azure ML here:
http://blogs.technet.com/b/machinelearning/

Read about 20 years of ML research at Microsoft here:
http://blogs.technet.com/b/machinelearning/archive/2014/07/08/twenty-years-of-machine-learning-at-microsoft.aspx

You start creating a service (presently in preview and therefore free)  in the Azure Portal (Data Services | Machine Learning).

 

The following are the items to create the Machine Learning service. You create a workspace by providing a name, herein, for example HodentekML. You have to specify a owner, which requires a Windows Live login. Since my account is in South Central US perhaps that is only option for me as far as the location is concerned. Since I do not have a Storage account, I will have to create a little later as you will see. I also have to provide a storage account name (something like 3-8 characters in all, only letter (small case) and numbers not using any special characters)

 

 

When you click CREATE AN ML WORKSPACE it will take you to the screen to create a Cloud Service.

In order to use Azure ML you need Cloud Service. If you do not have one, you need to create a Cloud Service to complete your Azure ML. The interactive windows takes you to creating a Cloud Service with a Quick Create link. Click on the arrow to CREATE A CLOUD SERVICE which takes you to next screen.

You need to provide the first part of the URL in the first box, herein MLPreview is entered, the URL is,
MLPreview.cloudapp.net

With this you will have created a Machine Learning Service.

Well this is just the beginning. Here is a must watch video in using AzureML