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Re-imagining the ‘digital’ workforce…

Re-imagining the ‘digital’ workforce… 1280 853 Administrator

While there is a lot of attention and focus on two aspects of “digital” i.e. the Technology and Use Cases (ala disruption & transformation), the third, and important, cog of the wheel hasn’t been paid too much of an attention. THE PEOPLE!!

Oh yes, there are a lot of conversations about continuous learning, upskilling, agile processes and the likes. Again, most of them are concentrated on the tech aspects of the factory model of churning out people with “those” skillsets.

If we can spend some time to understand the fundamentals of what this Digital does (now is a good time, because, now we think we know!) and how we can reimagine some of our roles, it might be worthwhile

What’s in an AI project?

Let us talk about how an AI/ML project gets delivered. (Here is where the purists need to pardon us!)

Most of the work we do in this space still is supervised. That means, we go through the typical steps – data collection, labelling, processing, running some (super) models on this data set, testing it on “hold-out” data and, viola, the model is built. Then comes of the process of exposing that model and consuming the inference part. And (thankfully) a few folks have started talking about ‘model management’ post deployment in production.

In this context, we only talk about the new breed of people; the data scientists. Today, there is a scramble to learn machine learning and AI, to become a part of this elite breed.

If we can pause and look at what skillsets are actually needed, beyond the model development (which a lot of people peg at just 20-30% of the overall work), we will see that the rest of it is data engineering. And, if we dive a little deeper, at each stage, it is about relatively simpler things like:
• Profiling the data to see if enough data and attributes are available
• Validating whether the data set that is being used is representative enough for the model / hypothesis
• Imputing or extrapolating and generating additional data

IoT project?

Let us move to a different digital implementation – IoT. This is all about huge volumes of data on a continuous basis. Again, a lot of things that we tend to underplay are what happens if there is a connectivity loss, what happens if we get corrupted data within the stream for a certain duration, what happens if there is duplicate data, and so on.

And the super cool Blockchain

Why leave Blockchain out of our discussion? Let’s take a quick look at that too. Beyond the actual SDKs, peer nodes, hashes, and immutability, there are other considerations like how much and what data do I store on the chain, what and where do I store the off-chain data, how do we manage the overall latency, how do I cater to de-duplication, and so on.

See the pattern?

Yes, there will always be specialists who are competent and capable of doing a specific piece of work. But how can the rest of the team rally around them, contribute and make it a successful implementation? While we try and upskill people from a technology standpoint and have them learn Python, Scala, Hyperledger, Spark, etc., there is a re-imagination that needs to be done in terms of their core skills and their contributions.

Let’s look at this through our traditional roles, like a Tester. This is the breed that has been on the automation journey for a while now. They constantly struggle with lack of test data, which kind of forces them to create their own data. Their very job description is about identifying multiple negative scenarios (gap in between data chunks, repeated data, performance) and test. If we can go back and look at what our AI/ML project needs today, there is a clear synergy in terms of leveraging this skillset.

Same is the case with our traditional Business Analyst. Why can’t they be the ones that call out the AI / ML hypothesis or that real Blockchain use case, and work with the developers in helping these see the light of the day?

You don’t have to look beyond our traditional DB developers / ETL Developers. They are the ones that kind of made that transition from ETL tools of the world to Spark, and are now being called Data Engineers!

To close out

Look internally, within the enterprise, at your existing team members. Maybe these are the folks, the knights in shining armor, who will finally help Digital projects launch successfully.
That is probably true re-imagination and re-alignment of our workforce.

And then a bit more…

This is an interesting and the most important aspect to follow-through. We will continue to expand on each role and see how it has and needs to transform in the current digital world. Keep watching this space.

Getting Started with Computer Vision on Windows Laptop

Getting Started with Computer Vision on Windows Laptop 800 526 Administrator

Computer vision is the future and you can’t wait to get started off on your windows laptop. I will give you the fastest way to get started off with just a few steps.  Just stay with me for the next 20 minutes and you should be on your way with doing Computer Vision using Deep Learning.

So, without further ado, here it goes…

Step 1:

Install Anaconda 3 on your laptop.

Just go to https://www.anaconda.com/distribution/#windows and download the distribution for the required version.

Once downloaded, just double click on the installer and you are on your way.

Step 2:

Let us get started with installing Tensorflow GPU for the deep learning part. Now conventionally you would have to go ahead and install CUDA followed by CuDnn and only then get to tensorflow-gpu package. Now just because of al the dependencies between them there would be a less than 1% chance that you would get it right the first time around.

Not for us, here is the magical command to do all that in one simple step.

Conda create –name tf-gpu tensorflow-gpu.

This would create a new virtual conda environment with tensorflow-gpu and all it’s zillion dependencies in one go!!

Just say conda activate tf-gpu and you are all set to start your computer vision deep learning journey with tensorflow.

Step 3:

Install Open CV and Open CV contrib, which are very useful for various morphological operations on images before feeding them to deep neural nets.

Conda install -c conda-forge opencv.

That is it….the entire power of OpenCV for you to explore!!

Step 4:

Last but not least install dlib. It is one of the best computer vision packages that I have seen. It is especially useful if you are trying to work in the facial recognition space – it just is the best that is out there.

Definitely not easy to install on windows – what with compiling all those C binaries, but then, let us use a bit of magic.

python -m pip install https://files.pythonhosted.org/packages/0e/ce/f8a3cff33ac03a8219768f0694c5d703c8e037e6aba2e865f9bae22ed63c/dlib-19.8.1-cp36-cp36m-win_amd64.whl#sha256=794994fa2c54e7776659fddb148363a5556468a6d5d46be8dad311722d54bfcf.

That is it folks!! You are all set now. Please let me know if this helped you, just to motivate me to put together other useful stuff to get you started faster. Ciao!!

And then so it happened one day…

And then so it happened one day… 1080 720 Administrator

That I decided I wanted to be a part of something that I did on my own and moved on from a great company like Microsoft to a startup and began my entrepreneurship journey. If only things/thoughts/ideas could be as simple and as quick as what that sentence summarises!!

So, here I am, a few months into a brand new year and at the biggest and most courageous crossroads of my life. How did I even get here?

Like a whole lot of people my age at that point in time, I got into engineering (since I felt qualifying and studying to become a doctor was tedious). But then, when I had a chance to be comfortable in my hometown and study the course that was a rage at that time, I chose to go to Warangal and studied Civil Engineering in NIT! That one decision, I guess, was the first important milestone that changed my life for good.

Post graduation, I joined a company called ValueLabs (then AppLabs) where quickly and steadily I moved up the ladder. “A company is what it is, because of the people that work there” – this couldn’t have been any more true than in this place.  At a stage in my career when things were very stable and I was a part of the team that was responsible for digital transformation, I found myself at crossroads again. Very unlike people in my career path, where changes in jobs were frequent, I stuck to the same company for a very long time. But then, the world of digital was enticing. I was on that high of trying to learn and understand and implement everything cool that came my way.

I decided I had to get to a place where the action is. And hence took up an offer in Microsoft as their Data & AI architect. The change was tremendous. A first time job change, a MNC that was a well-oiled machine, a role that changed me overnight into an individual contributor, a job that needed me to learn every waking moment (and gave me a few sleepless nights till I got to know I will never know enough!) It was all worth it… It gave me the confidence that I can deal with new technology, it helped me understand my strengths with a lot more clarity, it got me in contact with people that are extremely talented. It got me an opportunity to see technology up-close. And then suddenly things changed again.

Multiple rounds of coffees and teas and general discussions about purpose in life, one life to try and experiment, what do I have to lose, why now/why not now debates – just like that a bunch of us decided to go the dark side – start something of our own. While the decision wasn’t easy, the path from thought to actual reality was even more difficult. And so here I am at that crossroads once again looking forward to an exciting journey ahead. Before I move ahead and it becomes a day-day thinge, I wanted to list down what made me take this decision:

  • To try and solve interesting problems, in our own way – simple and to the point. And to see how far common sense can take us
  • To be fully, totally and completely responsible for all good and not so good decisions that I take in my professional life
  • To be unpretentious and work with people that I enjoy working with

RoundSqr it is going to be – digital pitstop is how we see our company being perceived as… And so it is Vuja De for me – something familiar but viewed with a fresh view. Lets see how far it takes me and you on this journey.

Auto Rickshaw Drivers and Corporate Strategy

Auto Rickshaw Drivers and Corporate Strategy 1440 1018 Srinivas Atreya

I live and work in Hyderabad, India and commute everyday to an area called HITEC City, which is the hub for Tech companies in the city. The traffic is pretty bad both in the mornings and the evenings, and it takes me close to an hour each way.

On a particularly bad morning, I had already done an hour in traffic and it looked like another 15-20 minutes were still needed to reach office. As I was waiting patiently, behind a sea of cars, to turn right at the signal, I saw a couple of auto rickshaw (https://en.wikipedia.org/wiki/Auto_rickshaw) drivers zig-zagging through the traffic. I got interested and, with nothing better to do, started watching them. They just drove wherever they could find an inch of space and finally managed to take the right turn way before I could (I had to wait for another traffic signal before managing the turn). Now, please do not get me wrong, I am not condoning their driving styles or anything, but still it got me thinking.

As entrepreneurs we spend a lot of time and effort in trying to define the strategy of our organizations, and for good reasons. We want to be pretty clear about the general direction our organizations are moving in. But then, what about the tactical opportunities that come our way? Taking the auto rickshaw driver analogy again, even though the driver knew he had to turn right, he did not hesitate going left for some distance when he saw that there was an opportunity to move forward!

In the present times, change is everywhere and it’s pace is only accelerating. Business models are being rendered obsolete in years rather than decades. No one is really sure anymore. What if our strategy is only to take on opportunities as they arise? We may still have a general direction (for example, work in the healthcare IT space or Waste Management space), but the specifics will be dictated by the available opportunities.

What if the winning strategy of the future is to just keep your eyes and ears open, and react to market opportunities as they present themselves? What if all the strategy that we really need is to prepare ourselves internally so that we can grab an opportunity as it arises?

Just some thoughts that I had over the last few days. Please feel free to leave any comments / suggestions. Thanks everyone for taking the time to read.