# Wednesday, 30 May 2018

Creating a virtual machine (VM) may be the simplest thing to do in Azure. Azure VMs are very similar to on-premises VMs; in fact, they are built using the same technology - Microsoft Hyper-V.

You can create an Azure VM with just about any operating system, software, and data that you want. Hundreds of templates are available, pre-installed with a variety of configurations.

Azure supports Windows machines and Linux machines. (Did you know that about a third of the VMs in Azure are running Linux?)

The first step in creating an Azure VM is the same step when we create any Azure resource - Navigate to the Azure Portal, log in, and click the [Create a resource] button (Fig. 1) at the top left of the portal.

Fig. 1

From the list of resource categories, select the "Compute" category; then select the template on which you want to base your VM, as shown in Fig. 2.

Fig. 2

If you don't see the VM you want, you can search for it by typing a partial description in the search box. This will display matching templates from which you can select, as shown in Fig. 3.

Fig. 3

After selecting a template, a blade with a brief description displays. Fig. 4 shows the blade for an Ubuntu server. Click the [Create] button at the bottom of this blade.

Fig. 4

The "Create virtual machine" dialog displays with the first tab - "Configure basic settings" - active, as shown in Fig. 5. Complete the forms on this tab.

Fig. 5

  • At the name field, enter a unique name for your VM. Choose one that will make it easy for you to identify it when you return to the portal to manage this VM.
  • At the "VM disk type" dropdown, select "SSD" for a Solid State Drive or "HDD" for a standard hard drive.
  • At the "User name" field, enter a name for the local administrator account on this VM.
  • At "Authentication type", select either "SSH public key" or "Password" to specify how you want to log into this VM. I have selected "Password", which causes the "Password" fields to appear, requiring me to enter a password twice. Passwords must meet complexity requirements, including a length of at least of at least 12 characters and inclusion of numbers, alpha characters, and special characters.
  • If you have an Azure AD configured, you can configure your VM to use this for authentication by selecting "Enabled" under "Login with Azure Directory".
  • You will likely have only one Azure subscription; but if you have more than one, you can select the subscription with which to associate this VM.
  • At the "Resource group" fields, select an existing resource group or create a new one. Resource groups are a logical grouping of resources that you want to manage together. If there are existing Azure resources that will interact with this VM, you may want to place them all in the same Resource Group.
  • At the "Location" dropdown, select the region in which you want to create this VM. Location considerations include proximity to the people managing this VM; proximity to people using this VM; and proximity to any other resources that will interact with this VM.

When the tab is complete, click the [OK] button at the bottom.

The second tab - "Size" - will display, as shown in Fig. 6.

Fig. 6

On the "Size" tab, select the size machine you want to create. This is a long list, but you can filter or search to make it more manageable. Note that not every size is available in every region.

After highlighting the desired size, click the [Select] button at the bottom of the tab.

The "Settings" tab will display as shown in Fig. 7.

Fig. 7

Azure provides default values for every setting on this tab, so it is not necessary to change anything. However, you can configure your VM to connect to a specific virtual network or subnet or assign a specific IP address or change any of the settings you like.

When you are satisfied with the settings, click the [OK] button at the bottom of the tab.

The "Summary" tab displays what you have configured, in case you want to change anything at the last minute. It also validates that all your selections are complete and consistent. If the validation lists any problems, you should go back and correct these. 

After validation passes and you are satisfied with your selection, click the [Create] button at the bottom of the blade.

After a few minutes, Azure will create and deploy the VM based on your configuration.

Once it is created, it is your VM and you can connect to it as you would any local VM. Use Remote Desktop to connect to a Windows VM or use SSH to connect to a Linux VM.

Azure | IAAS
Wednesday, 30 May 2018 21:46:00 (GMT Daylight Time, UTC+01:00)
# Monday, 28 May 2018
Monday, 28 May 2018 10:13:00 (GMT Daylight Time, UTC+01:00)
# Sunday, 27 May 2018

IMG_0659Two weeks ago, I attended my first Imagine Cup event - the US Finals in San Francisco. Yesterday, I attended my second.

The Canadian Imagine Cup Finals took place in Vancouver, BC. Six finalists competed for the right to advance to the International Finals in Redmond in July.

This event was smaller and shorter than the corresponding US event; but equal in energy. Every one of the teams showed great creativity, strong technical skills, and impressive presentations.

I was excited that all 6 finalists came from schools with which I work. First place went to SmartArm - a project that provides affordable prosthetic arms for amputees - a team I helped mentor at the UTHacks hackathon in January in Toronto.

I'm getting used to these Imagine Cup projects and competitions and I'm looking forward to the world finals.

Sunday, 27 May 2018 17:35:17 (GMT Daylight Time, UTC+01:00)
# Friday, 25 May 2018

One of the challenges of working with data is what to do with missing data.

A missing column in a dataset can be, but is not limited to the following:

  • No text or numbers between 2 column delimiters
  • An empty string ("")
  • A blank string (e.g., "   ")
  • The number 0
  • A special indicator, such as "NA" or "NONE"
  • An inconsistent data type, such as a number where a string is expected
  • A value that makes no sense in the context of the data.

The last one requires some domain knowledge about the data, so it is often difficult to spot.

There are two strategies for dealing with missing data

  1. Delete or ignore the entire row
  2. Replace the column with a reasonable value.

If only a few rows contain missing data, it may be efficient to simply delete these rows.

But if many rows contain missing data, it probably makes sense to keep them as other columns may contain valuable information. In this case, we will want to replace the missing data with a reasonable value.

But what is a reasonable value?

Options include replacing the column with an average value, such as the mean or median of the non-missing values. Of course, this is only valid for numeric data that is ordinal, that is data in which higher numbers indicate a higher value and not simply a discrete category.

The Pandas library contains some simple functions for deleting rows and replacing values. The fillna function is the simplest way to do this.

# Replace all missing values with 0

# Replace all missing values with the string 'Missing'

You can delete invalid or missing rows by overwriting a dataset with a filtered version of that set, as in the following examples

# Delete all rows with area = 0 
df = df[df.area != 0]

# Delete all rows with null area 
df = df[df.area.notnull()] 

But for values that are not missing, but are inappropriate (e.g., using 0 to represent a missing data point, when 0 could be a valid measurement), we can use the map function.

Below, we use the map function to want to replace any value in the 'area' column that has a value of 0 with the mean value for this column.

# Replace 0 area with the mean
mean_area = df['area'].mean()   
df['area'] = df['area'].map({0: mean_area})    

In this article, we discussed ways to use Pandas handle missing data in a dataframe.

Friday, 25 May 2018 00:44:34 (GMT Daylight Time, UTC+01:00)
# Monday, 21 May 2018
Monday, 21 May 2018 16:37:00 (GMT Daylight Time, UTC+01:00)
# Friday, 18 May 2018

I was working with a dataset in Azure ML Studio and I needed to replace values in a column.

Reasons for replacing value include:

  • Replacing codes with a more readable word or words
  • Consistency when combining 2 sets of data
  • Converting to numeric values in order to assign values to discrete strings
  • Converting to numeric values in order to work with an algorithm that only accepts numeric values

There is no built-in shape to do this, but you can do so with a couple lines of code.

I can demonstrate by creating a new ML studio experiment and dragging the "Automobile Price Data" sample dataset onto the experiment design surface, as shown in Fig. 1.

Fig. 1

If we click on this shape and select "Visualize" (Fig. 2), we can see the data in the dataset. Click the "drive-wheels" column to see details about the data in that column (Fig. 3).

Fig. 2

Fig. 3

You can see from the visualization that the "drive-wheels" column contains 3 distinct values: "fwd", "rwd", "4wd"
Imagine I wanted to replace these with "FRONT", "REAR", and "FOUR", respectively. (Maybe to be consistent with a second dataset I plan to merge with this one.)

Drag an "Execute Python Script" shape to the  Experiment and connect its input to the output of the data shape (Fig. 4).

Fig. 4

In the Properties of the "Execute Python Script" shape, replace the existing code with the following:

import pandas as pd
def azureml_main(dataframe1 = None):
    dataframe1['drive-wheels'] = dataframe1['drive-wheels'].map({'fwd': 'FRONT', 'rwd': 'REAR', '4wd': 'FOUR'})
    return dataframe1,

The azureml_main function is required by ML Studio. It accepts one parameter - a dataframe, which we name “dataframe1”

The first line of code maps the 3 existing drive-wheels values to 3 new values for every row and saves these 3 new values back to the dataset. By returning that dataframe, we make this updated data the output of this shape, so it can be used by later steps in our experiment.

After running this experiment, we can click the script shape and Visualize the output and see that each value in "drive-wheels" has been replaced, as shown in Fig 5.

Fig. 5

This article shows a simple way to replace values in a dataframe column with new values in Azure ML Studio.

Friday, 18 May 2018 22:15:08 (GMT Daylight Time, UTC+01:00)
# Thursday, 17 May 2018

Many Machine Learning solutions have the same steps in common. For example, you will need to retrieve data from one or more sources; you will want to split your data into training and testing subsets; and you will want to clean up your source data. I refer to these as "plumbing tasks” because they are common to so many projects; and spending time coding these tasks takes time away from working on your data and your solution.

Azure Machine Learning Studio can help.

Azure Machine Learning Studio or "ML Studio" is a graphical design tool for building machine learning solutions. It includes a design surface and a set of shapes to perform specific tasks.

To work with ML Studio, drag a shape onto the design surface, set some properties, and connect the inputs and/or outputs to other shapes to build the workflow of your solution. For example, you can drag an "Import Data" shape onto a form and set information about the data source and data type. This is "plumbing" code that you do not have to write.


If a shape for a desired task does not exist, there are shapes that allow you to write custom code. Supported languages are Python and R.

When you finish building and testing your solution, buttons at the bottom allow you to configure and deploy a web service, so that your model is accessible via a simple API. There is even a test page, allowing you to call this API from within your browser.

You can get a free trial at https://studio.azureml.net/

There are limits to the free version. You cannot configure the size and number of instances on which it will run, and you are limited to 10 GB storage. If you cannot work within these restrictions, you can sign up for an Azure account and pay for the resources you use. Current pricing is available at this link.


If you are looking for a quick and simple way to build a machine learning solution, Azure Machine Learning Studio may be the tool for you.

Thursday, 17 May 2018 23:58:00 (GMT Daylight Time, UTC+01:00)
# Tuesday, 15 May 2018

I have been working with North American universities since joining Microsoft almost 5 years ago.

The school year is winding down for most colleges, so I'd like to talk about ways that Microsoft is helping university students in North America.

Azure for Students

This offering provides $100 Azure cloud computing credit free to any student. It is a good opportunity to learn about cloud computing and test the services in Azure. You can sign up with an EDU email address at http://aka.ms/Azure4Students. No credit card is required.

  • $100 credit for 1 year
  • Access to all of Azure
  • No credit card required

Microsoft Student Partner

Microsoft Student Partners (or MSPs) serve as Microsoft advocates on campus, providing workshops and information about Microsoft technology. In exchange, they receive education from Microsoft and (sometimes) a few prizes. It is a good way to increase the networking, education, and employability of a student. You can learn more at https://imagine.microsoft.com/msp

Student Ambassador

This program is similar to the MSP program, but it is run by the Microsoft Recruiting organization.

Student Hackathons

Student hackathons have become very popular the last few years. At a hackathon, students get together on campus and build hardware and software projects in teams. Many hackathons offer prizes for the best projects and students from other universities are often welcome. Microsoft has sponsored a great many hackathons over the years, offering money, prizes, Azure credits, hardware, and mentors to answer student questions. I have personally been involved in dozens of hackathons the last 3 years.

Imagine Cup

Imagine Cup is an international competition for teams of students who build amazing projects and want to turn them into a business. The top teams in each participating country are invited to the national finals for a chance to pitch their projects to a panel of judges, who select a few teams to advance to the International Finals. The top prize for this competition is $100,000 US and a mentoring session with Microsoft CEO Satya Nadella. You can learn more about Imagine Cup at https://imagine.microsoft.com/Compete


Recently, I have been involved in a few DataFests. A DataFest is a competition on campus in which students are provided a set of data they have not yet seen; and asked to provide insights into the data. Students are free to use any tools they want and many present summaries, visualizations, and predictive analyses about the data. For the 3 DataFests in which I was involved (two at the University of Toronto and one at Duke University), Microsoft provided funds for food, free Azure credits, a workshop to show how to use MS's data science tools, and mentors to answer student questions.


Microsoft offers opportunities for university students to intern with the company. Most take place in Redmond in the summer. This is a great chance to work with a product team, learn new skills, and enhance your resume. These internships are very competitive, so students are encouraged to apply early in the school year. You can learn more and apply for internships at https://careers.microsoft.com/us/en/students-and-graduates

Academic CSE Team

This is the team for which I have been working this past year. We have coordinated and executed many of Microsoft's programs around university education. My responsibilities have included talking with professors, TAs, and students about how to incorporate Azure into their classes, meeting with MSP, and mentoring at hackathons and DataFests.

And So...

Microsoft is committed to helping students learn about software and computer science. The above list is some of the opportunities for students provided by Microsoft. Microsoft’s new fiscal year begins in a few weeks and there is not guarantee these programs will remain the same next year. In fact it’s likely there will be some changes.I don't know what programs will be offered going forward, but I expect a continuing strong commitment from my employer.

Tuesday, 15 May 2018 14:54:00 (GMT Daylight Time, UTC+01:00)
# Monday, 14 May 2018
Monday, 14 May 2018 09:33:00 (GMT Daylight Time, UTC+01:00)
# Sunday, 13 May 2018

Freddy Cole at the Jazz ShowcaseFreddy Cole looked every bit of his 83 years as he was helped onto the stage last night at the Jazz Showcase in Chicago's Printer's Row.

Until he sat at the piano. At that point he was transformed. For an hour and a half, he showed a strength and grace that belied his 8 decades. His command of piano and vocals was a strong as a man half his age.

He launched from one song to the next, never taking a break to chat with the audience until the final few minutes.

Although Cole can claim 3 Grammy nominations, he will always be remembered as the younger brother of legendary singer Nat "King" Cole. But he does not shun that comparison, as his set included three of Nat's songs (Paper Moon, L-O-V-E, and A Blossom Fell) and he recently released an album of songs made famous by his brother. Close your eyes and the richness of Freddy's voice is reminiscent of his late brother's talents.

Cole stuck mostly to ballads, but pleased the local crowd near the end of his set with a rendition of Ray Price's swinging "On the South Side of Chicago", which brought an ovation from his hometown.

His piano and vocals were accompanied by drums, upright bass, and guitar. Of course, the attention was mostly on Cole, but his talented Adam Moezinia took many solos. Bassist Elias Bailey stayed in the background until the last few songs when he became more and more bold with his solos and complex playing.

Freddy and DavidIn the end, Freddy Cole closed the set with a song called "Goodbye", accepted a standing ovation, and was helped from the stage, again transformed into a fragile old man. Until the night's second set.

Sunday, 13 May 2018 14:49:00 (GMT Daylight Time, UTC+01:00)