Month: September 2018

Wow, it has already been almost a month since I started!

My new playground covers IoT and AI, and I am supposed to have a broad understanding of both.

Regarding IoT, my recent background helped me grow a solid groundwork for that. I am fairly comfortable with the concepts, and with the involved technologies. Moreover, I have a colleague whose sole purpose is to understand and build IoT solutions, so my bases are well covered.

When it comes to Artificial Intelligence, the coast is less clear.

First, it is not a domain where I have any background, neither in the theory (math, bio science…) nor practical (any implementation of AI).

Second, AI is the 2018 version of the Cloud in 2014 : everyone wants to do it, but not one has a clear definition of what we are talking about.

Last but not least, the very term AI covers almost anything, from a chatbot to augmented reality to self-driving cars.

My process has been a bit convoluted so far.

First thing I have tried was to register for e-learning (MOOC or otherwise) sessions on the topic. I have tried several, from OpenEDX to Microsoft AI school, to Google and Tensorflow. The content ranged from very high level, which was mostly too high for me, to algebra (which was a bit too deep for me).

Then I tried to read about the market. So I read a lot of whitepapers, from Microsoft, from DataIKU, from Forrester etc.

This was rather useful, as it gave me basic understanding of where the situation was.

I recommend Dataiku Machine Learning Demystified : https://pages.dataiku.com/machine-learning-basics-illustrated-guidebook

But still, I felt I was stuck in the theory and could not find the practical applications.

After some discussions with my usual suspect, Microsoft, I did have a look at their business uses cases and testimonies.

I have to admit, some of them were pretty interesting… however there is absolutely no information about the architecture or implementation of the solution, which left me wanting.

I finally found two Microsoft websites who did a good job of describing architectural templates, along with potential uses cases.

https://azure.microsoft.com/en-us/solutions/architecture/?solution=big-data

https://docs.microsoft.com/en-us/azure/architecture

This is where I started digging, and it made my mind spin with all the possibilities. You will have to wait a bit for the outcomes, and follow what SCC will be doing on this market in the coming weeks 😉

Last note, one of the smartest guy I have met at Microsoft, Frederic Wickert has started an AI business, and is writing, in French, to help debunk AI for us. I definitely recommend reading his posts! I admit I have not yet read the whole post, to avoid repeating everything here 😉

Nope, my new position is not dead yet, thank you very much.
What I mean by this title is usually a meeting in any IT service, after a major incident has been resolved, where all the team members who have worked on the incident gather and discuss what went wrong, and how to improve tools and processes to do better next time.

I specify blameless, as it is a very good practice to avoid finger pointing, generally and particularly in these meetings. If you want people to be honest and share their best insights, you have to keep in mind that these post-mortems have to cultivate an atmosphere of trust. The aim is really to find out how the events have unfolded, which information had been gathered, what went wrong, what steps were smart, which ones did not work properly etc.
For more information about that, I recommend some DevOps sessions and talks, like this one from @Jasonhand from VictorOps : It’s Not Your Fault – Blameless Post-mortems

But my point today is to write about another kind of post-mortem which I discussed with a friend a few months back.
The methodology of a post mortem could and should be used in different settings than just IT infrastructure incidents. It should be extended to sales, whether you manage to win or lose the deal. It could be applied personally to any job interview, even if there are usually not that many people involved. And it could be used after any major event in your life, personal or professional.

The main focus for me right now would be the sales post-mortem. In most companies I have worked for, the sales pipeline strategy is mostly to respond to as many RFP as possible. Statistically, it makes sense, as you are doomed to success every once in a while. In terms of smart strategy… let’s say I am not completely convinced. I tend to prefer a targeted answer to the cases where my team/company can bring out real value and help the customer while bringing attractive project to our team. I usually do not hesitate to forgo any RFP where there is nothing interesting or that puts us in jeopardy without bringing any value, or sexiness to our job.
When you have time to focus on very interesting cases and invest time on those, you would usually find this time useful, on short and long term. And you should take time, whether you win or lose, to have this post-mortem meeting with your team. It is good to get the feelings and insights from everyone involved about the outcome. And I mean everyone. The first stakeholder you should at least get feedback from is the customer. I try to build a trust relationship with a potential customer during the RFP process where we can exchange honest points of view about our positioning and the project expectations. During the process this helps everyone stay on the right track. And afterwards, it helps to know why you have not been chosen.

Beyond knowing, the most important aspect of these post-mortems is to implement some changes on your process, to be more relevant and have a better chance for success the following time around.

And that’s it for early morning musings, ’til next time!