#MSBuild Azure Open Datasets

Nearly three years ago, I complained bitterly about the demise of Windows Datamarket, which aimed to provide free, stock datasets for any and every purpose. I was a huge fan of the date dimension and  the geography dimension, since they really helped me to get started with data warehousing.

So I’m glad to say that the concept is back, revamped and rebuilt for the data scientists today. Azure Open Datasets will be useful to anyone who wants data for any reason: perhaps for learning, for demos, for improving machine learning accuracy, perhaps.

The purpose of Azure Open Datasets is to save data scientists time and increase productivity by saving the time normally spent on both data discovery and

Azure Open Datasets is available in preview, so why not take a look? Datasets are cohosted with cloud compute in Azure making access and manipulation easier. They are contained in Notebooks, which is even better!

To learn more, join my Build session on Wednesday 8th May at 2pm in the Sheraton Grand Ballroom D. I’ll show you how it works.

Please head over and give it a try. I’m happy to see the concept is back.

#MSBuild Announcements: Making your AI Easier with updates to Azure Machine Learning, #MLOps, or #DevOps for Machine Learning

I’ve been using Azure Machine Learning for some time, and I’m excited about the new possibilities with the new updates to Azure Machine Learning to make it easier for AI novices to build, train and deploy machine learning models. I believe in the power of AI, and I believe that everyone should get a chance to use it.

Microsoft are helping businesses to have easy access to AI, from conception to modelling through to business value creation and sustainability by making it production-ready. Azure Machine Learning helps data scientists and developers build and train AI models faster, then easily deploy those models to the cloud or the edge. By simplifying AI, it makes it easier to derisk to get started with it.

Microsoft have announced a new automated machine learning user interface which is zero-cde, meaning that you create your models visually by dragging and dropping. I’m also pleased to be delivering a Build session on Wednesday at 2pm, where I get to show off the new machine learning notebooks for code-first model development.

Right now, these capabilities are available in preview, so why not head over and have a play?

There are also new capabilities to help you to transition your models to production, at scale. New MLOps, or DevOps for Machine Learning capabilities, simplifies the end-to-end lifecycle from model creation to deployment. In order to help make AI easier for the business to manage, it’s also possible to monitor it with Azure DevOps integration. If you want to know more about MLOps, check out this video with Seth Juarez (Twitter), who is fantastic at explaining things and the video is well worth checking out.

At Build, it was also announced that there is now high-speed inferencing from cloud to edge. This enables low-latency and lowcost inferencing with the general availability of hardware-accelerated models that run on FGPAs in Azure. This capability is also available in preview in Data Box edge. ONNX Runtime support for  NVIDIA TensorRT and Intel nGraph enables high-speed inferencing on NVIDIA and Intel chipsets.

To summarise, Microsoft are helping businesses to have easy access to AI, from conception to modelling through to business value creation and sustainability by making it production-ready. Azure Machine Learning helps data scientists and developers build and train AI models faster, then easily deploy those models to the cloud or the edge.
The updates in Azure Machine Learning are all currently available.

European Data Science & AI Awards 2019 Entry details

The European DatSci & AI Awards in collaboration with the BDVA & CeADAR are now accepting entries! 

 I’m delighted to announce that I am on the Judging Panel this year, along with people that I admire.

10 Awards to compete in The European DatSci _ AI Awards 2019

The Awards recognizes the gold standard for Data Science & AI in Industry, Education and Social Responsibility and connects the Data Science community across Europe.

The competition is open teams and individuals within the Data Science and AI community from across Europe.

Entry deadline for the competition is on the 24th May 2019.The competition is free to enter, so check out the criteria here and feel free to share. If you are interested in learning more sign up to the DatSci mailing list.

Check out the 2019 Categories

  • Data Scientist of the Year
  • Data Science Technology Innovation of the Year
  • Best Application of AI of the Year
  • Best use of Data Science/AI for Customer Experience
  • Best use of Data Science/AI for Health & Wellbeing
  • Best use of Data Science/AI for Industry 4.0
  • Best use of Data Science in SME/Start Up
  • Best use of Data to Achieve Social Impact
  • Best technical advance in the field of Data Science/AI from a research organisation either in academia or industry
  • Data Science Student of the Year

Important dates for this year’s Awards: 

  • March 2019 – 24th May 2019: Entry window open
  • June 2019: Judging & Finalists selected
  • July 2019 – Finalists announced
  • w/c 22nd July 2019: Finalists presentations
  • 5th September 2019: Awards Day & Winners announced

Business Analytics MSc Scholarship Available: 
A central initiative of the European DatSci & AI Awards is paying it forward to the next generation of Data Science Talent, each year proceeds of ticket sales fund a Scholarship for full-time fees for a Level 9 MSc Business Analytics Student at UCD Smurfit School, Dublin. Check out details here. 

Good luck!

10 Awards to compete in The European DatSci _ AI Awards 2019

The Strategist: Why Business Canvases aren’t enough if you don’t consider value creation

The Strategist: Be the Leader Your Business NeedsThe Strategist: Be the Leader Your Business Needs by Cynthia Montgomery

My rating: 5 of 5 stars

The value of this book lay in its ability to distill important, insightful points in a digestible format.

In The Strategist: Be the Leader Your Business Needs, Montgomery helps you to think about applying and understanding the market forces in your industry. Montgomery also discusses the importance of creating value and defining purpose with her Strategy Wheel. Here is an example Strategy Wheel:



The heart of all this is the purpose; why does your company exist? The book is about taking ownership of the process, and ensuring that your system of value creation is critically linked to your purpose. If organizations want to be more effective, efficient, and have more impactful, then the strategist needs to line things up in that direction. If it isn’t working in favour of your purpose and value, then cut it. The book is about identifying that strategy is about having a compelling purpose for why the organization exists, and ensuring that your organization is squared up to meet it, and push it forward.

From time to time, I see people not owning their behaviour. I also see them not owning their industry and understanding everything about it. The book had good case studies, where you could see people straying outside their red lines. Case Studies are all very well since we can look at them with cold objectivity. With our own business, it becomes less clear and it starts to engage our lizard brain, which is harder to master.

I re-read the Strategy Wheel chapter a few times. The danger with canvasses such as the Business Canvas (or rebadged attempts at it) is that people really don’t always ask themselves about value creation. It is supposed to be a core component of the Business Canvas model but I don’t always see it applied. Perhaps because it is the hardest part? It is easy to tick boxes in a dilettante fashion, and not think more deeply from there. Thinking about strategy and value is hard, and Montgomery argues that you have to move deeper than ticking boxes, and I think that she is right. I prefer the Strategy Wheel since it means you have to focus on your purpose, and I will be using a version for it for my AI for the Executives Masterclass in London in May. Register Here

The book is heavily Porterian, which is not surprising since Montgomery is also at Harvard. It means that people without a business knowledge backgound could understand the impacts in a Porterian fashion, but not necessarily know his theory. I think that makes it applicable and relevant to a wider audience, and that’s a good thing.

The Strategist: Be the Leader Your Business NeedsThe Strategist: Be the Leader Your Business Needs

View all my reviews

Empathy and Emotional Intelligence: Your ‘must have’ tools for #Leadership and what we learn from #Theranos

In the book ‘Bad Blood’ by John Carreyou, Theranos founder and CEO Elizabeth Holmes was widely seen as the female Steve Jobs: a brilliant Stanford dropout whose startup “unicorn” promised to revolutionize the medical industry with a machine that would make blood testing significantly faster and easier. Backed by investors such as Oracle’s Larry Ellison and the bitcoin bull and capital investor, Tim Draper, Theranos sold shares in a fundraising round that valued the company at more than $9 billion. Holmes achieved her life’s ambition of becoming a billionaire, with her worth at an estimated $4.7 billion. A major problem led to the largest corporate fraud since Enron: The technology didn’t work.

I read the book as a data professional, horrified by the lackadaisical approach to data governance, testing, and repeatable, testable science. Data was almost irrelevant, and it is clear from reading the book that the authorities, such as the FDA, hammered Theranos for their failure to put safeguards around their testing and data processes. So where does empathy come in?

Empathy is the art of remembering when others have helped make you feel heard, and empowered, and then paying that feeling forward to others.

On a deeper leadership level, it was clear that there was little emotional intelligence or empathy. It showed in a few things, such as a clear inability to empathise with the patients who relied on blood testing for their lives, as well as the military personnel who were one of the target audiences for this faulty machine.

One of my favourite quotes is Maya Angelou’s insightful comment:

“I’ve learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

As a leader, becoming empathetic is one of the most complex skills to master. From my reading of the Theranos situation in Bad Blood, it became clear that there was no atmosphere of empathetic leadership; in fact, quite the opposite. The people with the skills and data seemed afraid to speak to the Theranos leaders, and the book describes their feeling of terror when speaking with the leaders. It sounds like a hostile, sick place of work. If only they’d listened, perhaps Theranos might have had some of the successes that it was posited to have.

Earlier in my career, I used to have a complex reaction when people gave me unwarranted advice, or advice that I didn’t ask for. Sometimes I thought that they thought I was an idiot, or I didn’t know what I was talking about.

Now, as a leader, I realize that people felt that they could provide me with feedback; they weren’t afraid to talk to me. Now, I realize what a gift I’ve been given, and I appreciate it now. Thank you to everyone who shared their advice and wisdom with me. Possibly, I was not grateful at the time, but I see now that you felt you could talk to me.

Reading Bad Blood was a source of reflection for me, since it made me think about myself, and my responses to other people. If I had worked at Theranos, I would have been afraid to speak out, and I’d have probably just left.

What Holmes and Balwani missed out on was the gift of advice and thoughtful, constructive criticism for other people. People didn’t seem to be able to talk to them, so Holmes and Balwani never received their insights and help.

On reflection, sometimes I find myself in the situation where I could speak to someone with some insights, or even to warn them. But I can’t, because that person is simply too difficult to deal with, and I have to make a judgement call between making an effort to go through the pain of having to deal with them, and deal with the response of their lizard brain when they default to type, and don’t listen. So I leave it, step back, and leave them on their merry way to make mistakes. After a while, it’s just not worth my time and effort if I’ve bothered to try to engage.

I also realized that I cannot stomach a ‘make it until you fake it’ approach. I am not a dilettante, dabbling and making things sound good. I could see the dilettante, ‘fake it unti you make it’ approach resonate throughout the book and I realized how much it switches me off, and pushes me away. I am not looking for the good in people, I am looking for the real.

So I learned a lot from the book, about lack of empathy and emotional intelligence, but also about my response to people like that. I have been actively trying to grow my emotional intelligence and empathy, and here are some suggested reads. Click on the book for a link:




Enjoy! If you have any other recommendations, please leave them in the comments.

List of Python Colours for handy reference

This is a list of colours from colors.py for handy reference.

Color HEX
aliceblue F0F8FF
antiquewhite FAEBD7
aqua 00FFFF
aquamarine 7FFFD4
azure F0FFFF
beige F5F5DC
bisque FFE4C4
black 000000
blanchedalmond FFEBCD
blue 0000FF
blueviolet 8A2BE2
brown A52A2A
burlywood DEB887
cadetblue 5F9EA0
chartreuse 7FFF00
chocolate D2691E
coral FF7F50
cornflowerblue 6495ED
cornsilk FFF8DC
crimson DC143C
cyan 00FFFF
darkblue 00008B
darkcyan 008B8B
darkgoldenrod B8860B
darkgray A9A9A9
darkgreen 006400
darkgrey A9A9A9
darkkhaki BDB76B
darkmagenta 8B008B
darkolivegreen 556B2F
darkorange FF8C00
darkorchid 9932CC
darkred 8B0000
darksalmon E9967A
darkseagreen 8FBC8F
darkslateblue 483D8B
darkslategray 2F4F4F
darkslategrey 2F4F4F
darkturquoise 00CED1
darkviolet 9400D3
deeppink FF1493
deepskyblue 00BFFF
dimgray 696969
dimgrey 696969
dodgerblue 1E90FF
firebrick B22222
floralwhite FFFAF0
forestgreen 228B22
fuchsia FF00FF
gainsboro DCDCDC
ghostwhite F8F8FF
gold FFD700
goldenrod DAA520
gray 808080
green 008000
greenyellow ADFF2F
grey 808080
honeydew F0FFF0
hotpink FF69B4
indianred CD5C5C
indigo 4B0082
ivory FFFFF0
khaki F0E68C
lavender E6E6FA
lavenderblush FFF0F5
lawngreen 7CFC00
lemonchiffon FFFACD
lightblue ADD8E6
lightcoral F08080
lightcyan E0FFFF
lightgoldenrodyellow FAFAD2
lightgray D3D3D3
lightgreen 90EE90
lightgrey D3D3D3
lightpink FFB6C1
lightsalmon FFA07A
lightseagreen 20B2AA
lightskyblue 87CEFA
lightslategray 778899
lightslategrey 778899
lightsteelblue B0C4DE
lightyellow FFFFE0
lime 00FF00
limegreen 32CD32
linen FAF0E6
magenta FF00FF
maroon 800000
mediumaquamarine 66CDAA
mediumblue 0000CD
mediumorchid BA55D3
mediumpurple 9370DB
mediumseagreen 3CB371
mediumslateblue 7B68EE
mediumspringgreen 00FA9A
mediumturquoise 48D1CC
mediumvioletred C71585
midnightblue 191970
mintcream F5FFFA
mistyrose FFE4E1
moccasin FFE4B5
navajowhite FFDEAD
navy 000080
oldlace FDF5E6
olive 808000
olivedrab 6B8E23
orange FFA500
orangered FF4500
orchid DA70D6
palegoldenrod EEE8AA
palegreen 98FB98
paleturquoise AFEEEE
palevioletred DB7093
papayawhip FFEFD5
peachpuff FFDAB9
peru CD853F
pink FFC0CB
plum DDA0DD
powderblue B0E0E6
purple 800080
rebeccapurple 663399
red FF0000
rosybrown BC8F8F
royalblue 4169E1
saddlebrown 8B4513
salmon FA8072
sandybrown F4A460
seagreen 2E8B57
seashell FFF5EE
sienna A0522D
silver C0C0C0
skyblue 87CEEB
slateblue 6A5ACD
slategray 708090
slategrey 708090
springgreen 00FF7F
steelblue 4682B4
tan D2B48C
teal 008080
thistle D8BFD8
tomato FF6347
turquoise 40E0D0
violet EE82EE
wheat F5DEB3
white FFFFFF
whitesmoke F5F5F5
yellow FFFF00
yellowgreen 9ACD32

DIY Deep Fakes: an alternative point of view

I wanted to offer some alternative thoughts on the presentation entitled ‘DIY Deep Fakes‘ with the subtitle ‘Why Deep Fakes are dangerous, and how to make them‘. I don’t represent the Microsoft MVP Program, any other Microsoft program, or Microsoft. The backstory is that the presenter, James Ashley, was an MVP for ten or so years, and he was removed from the Program as per his blog post here. I have not met James although I’m a Microsoft MVP, holding the Award for 8 years.

The title of the presentation is DIY Deep Fakes. Straight away, that’s a call to action: literally, ‘do it yourself’. The first part of the presentation, James rightly points out that there are bad aspects to deep fakes, specifically, political objectives and pornography. Then, James walks you through the technology on making deepfakes, as per the subtitle. From the 37th minute to the end, the recommendation comes to try FakeApps with your browser in ‘incognito’ mode, antivirus on, and machine not connected to the network. For the record, I am absolutely NOT recommending that you create deepfakes. If you want to learn about AI, there are plenty of other fun, safe ways.

Let’s look past the presentation for a moment, and consider the consequences.  It’s not a huge jump to imagine that someone watching that would think, hey, why don’t I try that thing that the MVP did by myself? And before you know it, they’ve created a deepfake porn video, using Microsoft technologies, inspired by a Microsoft MVP, a well-respected community leader. Personally, I don’t believe that Microsoft would want that. The consequences could be tragic. In the video, James specifically calls out some of the virtual machines on Azure, from 33 minutes in the video for this purpose.

What could the presentation have achieved instead? The presentation could have shown more clearly how to identify a deepfake, how to report it if it is hurtful, and how to technically distinguish a truth from a lie. The presentation could have used the time and communication opportunity to do something to help combat this pernicious misuse of technology, and do good something really positive for community health, diversity and inclusion. I would like to have seen MVPs inspire a culture of positivity by clarifying how to catch deepfakes, and speak out forcefully. Instead, we get a presentation from an MVP about how we can make our own deepfakes and your title is literally a Call to Action on making deepfakes: Do It Yourself.

I don’t know the situation about James being removed from the MVP Program, or what happened, and all I have to go on is the presentation that James has tweeted, and James’ blog here. And, having reviewed both, that’s all I have to go on, and, quite frankly, I have no idea why this presentation was never questioned by anyone. Why not go for the technical and social challenge of preventing them in the first place? I’m all for open debates, but the presentation circumvents the debates by showing people how to create them; foregone conclusion.

James is right on one thing; anything along the lines of revenge porn, porn without the consent of the participants, porn created to hurt people, deepfakes etc etc are absolutely painful. When I was at university, one of my classmates photoshopped my face into the body of a porn actress, and printed out tons of copies and put them on the university dorms and they refused to take them down. Twenty years later, I still cringe when I think about it. I only found out about it because the male students all sniggered when I went past and eventually one of my friends told me, and I went to visit one of the rooms and there ‘I’ was – up on the wall. It was beyond horrifying. When I close my eyes, I can still see the picture. And that was just a picture. An actual movie would be much, much worse, and why oh why would that be given airtime? Why should I have to sit in conferences where someone is literally showing how to create deepfakes?

On a separate note, I have written about my own MeToo experience here and other places (it carries a trigger warning) and how the technical community participation has helped me in my healing process. I’d have loved it if my fellow MVPs would show support by helping to stop the problem, which is an interesting and technically complex challenge. I’d want to feel that the MVPs are on my side, as a fellow MVP and MeToo survivor/campaigner, and leading the community away to calling out deepfakes.

If you James or anyone else does respond, I hope that you’ll consider wisdom and good judgement in making a considered response. To quote Aeschylus:

“Even in our sleep, pain which cannot forget falls drop by drop upon the heart
until, in our own despair, against our will, comes wisdom through the awful grace of God.”

My wisdom is hard won, and I hope that this perspective will be considered along with all of the heat that seems to be happening. It seems as if the argument has got down into the weeds of simply apologizing to anyone who has seen the video, or attended the presentation at Summit, and who was upset by it. I have to say, the argument does not consider the impact of our voices as MVPs and as a community leader. Our voice carry as an MVP and I suspect many people would be horrified if someone made a deepfake porn as a result of this session. I’m not sure that this consequence was ever considered.

I don’t want to have to attend conferences where a well-respected speaker is showing audience members how to make deepfakes. They can get that information from anywhere on the Internet, sure, but the problem is, as a community leader (MVP or whatever) we set direction and tone. MVPs can help move the needle, and I’d have liked MVPs to be the people who speaks out forcefully and with conviction about the pernicious misuse of this technology, and helps move the needle for good. It’s not about creating them as a beautifully technical experiment, it’s about stopping the hurt that they can cause.

I hope that people in the technical community will consider the consequences of the DIY – literally, Do It Yourself – talk because the consequences go far greater than just potentially upsetting one of the immediate attendees. It’s the principle and the spirit that’s wrong. I don’t speak for MIcrosoft, the MVP Program or anyone else; this is my thoughts. For me, MVPs are given a great platform and opportunity to do something really great. Stopping deepfakes is hard, and perhaps the session should have been about that instead; still a great technical session, but one that really sets the tone as a great example of community leadership.

People have to think ethically and carefully about technology and it’s use and misuse, and who can get hurt, and how it could be stopped. I don’t think it’s right that we see lots of deepfakes inspired by MVPs. I think we need to strive to show wisdom and judgement as the leaders that the MVP Program recognizes us to be.