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Aligning Curriculum to Industry Requirements

Aligning Curriculum to Industry Requirements 1600 1600 Administrator

This blog is intended to share my thoughts and experience at University of Hyderabad (UOH) – that encompasses special meeting details; and interactions with great academicians across the country with a purpose in mind.

Today, businesses are putting in a lot of effort to train their employees. This is due to the mismatch between the industry expectations and the academia ideologies. Having been a part of the IT Services industry for 2 ½ decades, I share the Industry’s disillusionment about the job readiness of the students entering the workforce. However, the rapid pace of change in technology and customer expectations are compelling industries and academia to come together to address this issue.

Thanks to the University of Hyderabad (UOH) for breaking the boundaries. This year, UOH intends to take Industry feedback (experts’ opinion) to design their advanced MBA courses (including content, approach, and so on) that match the industry and market requirements.

We at RoundSqr have been invited by them to participate and give suggestions for fine-tuning this year’s curriculum for their executive MBA program (which is yet to be started).

I represented RoundSqr in the meeting. It has been a memorable experience for me to share the space with the most eminent professors from Indian Institute of Management (IIM) and Indian Institute of Technology (IIT), as well as stalwarts from the Industry (specifically TCS and Virtusa). We also had senior professors and other academicians from UoH in the meeting.

The team went through the current curriculum and did a detailed review. The meeting went on for three hours. After discussions related to topics at hand (like curriculum, subjects, schedule, credits, credit optimizations, course content etc.), we provided valuable suggestions to the University.

The enormous experience of academicians, deans and professors was clearly visible in terms of correlating the syllabus with that of practical considerations, student psychology, and industry relevance.

As industry representatives, we appreciated the academic aspects of the course, and their relevance in the industry scenario. However, we provided a few suggestions to walk the fine line between curriculum and industry requirements.

Some of the exploratory action items that were suggested by us to the University management for their consideration…

  • Course credits
    • Cut down the credits from 108 to 102 (as per ‘All India Council for Technical Education (AICTE)’ stipulated norms)
    • Have 4 credits per course | 6 courses per semester
    • One course per semester may be given as independent study, or the student may be advised to take up open online courses (specifically Swayam or Moocs). However, the end test may be conducted by the University only
    • Rename course titles in a more non-conventional manner
    • Introduce flexibility in course completion process (to give an option for the student to complete the course within four years after registration). However, the student should pass 50% of the courses in each semester to become eligible for the next semester)
  • Projects and electives
    • At the end of first year, students should do a project internship (for 6 weeks) in any organization (the nature of the work should be beneficial / impactful to the organization)
    • Proposed dual elective structure (dual course specialization) in the second year. Each elective will have two papers per semester
    • By the end of the final semester, student should do a major project in chosen elective area (this includes report writing and submission; and viva-voice) | 6 credits for the completion of this course
  • Pedagogical practices
    • Include case studies, rigorous assignments, interface with industry personnel, and sharing of experiences, etc.
    • The evaluation pattern distribution would be 60:40 (where 60% weightage is for the end semester exam; and the remaining 40% may include quizzes, term paper presentations, and assignments)
    • Follow interactive classroom teaching methods or techniques

To sum up

The alignment of curriculum with that of industry requirements is no longer an option, but a necessity. So, academia and companies should come together to address and solve the real-world challenges in this fast-paced environment. If this happens, businesses can save a lot of money, time and efforts. We can bring about a tremendous change in education sector as well.

When IA meets AI!

When IA meets AI! 1080 1080 Administrator

Circa early 2000s: An Accounts Payable staff would come into the office every day, read his or her emails, open the attachments (MS Excel, PDFs or others) and manually copy relevant data from these attachments into an ERP system.

Circa 2017: An Accounts Payable staff would come into the office every day, read his or her emails, opens the attachments (MS Excel, PDFs or others) and manually collates the data into a single format. Then the bots take over, validate and insert the data into an ERP system.

Cut to today’s date: An AI-driven bot first collects and consolidates all the information from varied sources (different kinds of Excel files, PDFs, scanned images, etc.) and then also copies the relevant information into an ERP system. The Accounts Payable staff is reviewing the process regularly and making necessary corrections, if required.

Most of the data, approximately around 70-90%, is dark data (semi-structured and unstructured). Most companies are still dealing with PDFs – these could be contracts, invoices, purchase orders and so on. If you are a large manufacturer, chances are you will have thousands of suppliers, each with a unique invoice layout! This semi-structured data is hard to process by rule-based systems, but if you bring AI into play you can deal with this semi-structured data.

Today is the day and age of combinational Artificial Intelligence and Intelligent Automation (fully supported by humungous computational power). The maturity of AI / ML and its ability to tackle a wide range of business problems, courtesy the advances made in Natural Language Processing, Object Recognition, Document Analysis and Classification, have made end-to-end automation possible. Just in the Document Analysis space, what OCR could do with at best 50-60% accuracy, an advanced AI-driven system could get to 90-95% precision, by using supervised and non-supervised machine learning.

The limitation that RPA had, all this while, was around decision making. The rule of thumb has been that for any rote or repetitive tasks, RPA is the way to go. But now, with the power of AI / ML, even decision-making and intelligence can be automated. This is more so in the case of unified tools that the likes of Automation Anywhere provide.

This is where things become enterprise ready – RPA task bots for rote tasks, IQ Bot for intelligent data extraction, and Control Room for monitoring / managing all the bots.

We can now truly say that RPA is ready to solve much bigger industry-grade problems, ushering us into the fourth industrial revolution – where software automation will be strategic, transformative and easily scalable!

Not convinced yet? Then the following forecasts should do the job – according to a Forrester report, the market for RPA will be valued at $2.9 billion by 2021 – a massive jump from the $250 million reported in 2016 (https://www.forrester.com/report/The+RPA+Market+Will+Reach+29+Billion+By+2021/-/E-RES137229)

At RoundSqr, we believe that the next big thing is convergence of Intelligent Automation and Artificial Intelligence. This will open up new frontiers, creating a state-of-the-art Digital workforce!

Let us take out the robot from the human:)

 

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.