Interesting reads - Data, Analytics, Security, DevOps, Python

Here is some interesting reading material I came across today - related to Data, Analytics, Security, DevOps and Python

• AI & Analytics in Production  
• Case Studies: Machine Learning
• Putting the Power of Kafka into the Hands of Data Scientists
• Building a Real-Time Bike-Share Data Pipeline with StreamSets, Kafka and MapD
• Real-time data foundations: Kafka - The what, why, and how of Kafka
• A Short Summary of Smoothing Algorithms
• AVA: The Art and Science of Image Discovery at Netflix
• Deploy only what you trust: introducing Binary Authorization for Google Kubernetes Engine
• A Clean Start: Finding Vulnerabilities in your Docker Base Images
• The Best User Behavior Analytics for Your Web or Mobile Product
• Mozilla identifies 10 open source personas: What you need to know
• 18 Python programming books for beginners and veterans
• Making the Adobe Cloud More Resilient — with Chaos
• 6 Patterns for Continuous Delivery with Serverless Applications
• How Kubernetes Is Transforming into a Universal Scheduler
• InSpec: Auditing and Testing Framework
• ChaoSlingr: Introducing Security into Chaos Testing
• Retire.js - What you require you must also retire

Happy Reading to you!

Testing in the Agile World

Thanks to ThoughtWorks, I was introduced to many things - 

• Agile
• Blogging
• Speaking at conferences
• Contributing to Open-source
• ...
• ...

The list is actually quite long - but that is not the intention of this post.

The main takeaway in my learning at ThoughtWorks though, is how to Test better, and be more effective in that for the end-user. 

Even before my time at ThoughtWorks, I never agreed with the thought process that Functional Automation can / should be done only when the feature is stable. But at ThoughtWorks, I did learn many more tips and tricks and techniques and processes how to do this Functional Automation in a better way, as the product is evolving.

On 9th April 2011, I had written a detailed blog post / article regarding how can we test better in the Agile world. 

This post was titled - "Agile QA Process", and the document was uploaded to slideshare with the name - "Agile QA Process". I am very pleasantly surprised that till date, that document has had over 74K views and almost 2.7K downloads, and is still my topmost viewed post on slideshare.

When I look back at the document, it still seems very relevant and applicable, to me! 

What do you think?

Some good examples of Data Science, AI & ML

Following up on my earlier post about ODSC - Data Science, AI, ML - Hype, or Reality?, I thought it is good to also share some of the good examples of work happening in the field.

Here are some of the examples I got to hear in the ODSC conference, most of which are available to the common human:

• Amazing work done in the complex field of Speech recognition 
• Why complex? Think about languages, dialects, multiple conversations at the same time, different speed of talking, etc.
• Text to speech
• Ex: This is especially very helpful for people with disabilities
• Speech to Text
• Ex: Alexa, Google Voice, etc. type of applications
• Traffic control / Routes / Navigation
• Ex: Google Maps
• Recommendation engines
• Ex: eCommerce products
• Preventive maintenance
• Lot of advanced vehicles have a number of sensors that can alert the driver / car manufacturer about potential issues coming up / service due for the vehicle
• Autonomous vehicles
• Ex: Self driving vehicles
• Ex: Optimizing Cab scheduling / routing - There was a good session on how OLA manage its complexity in scheduling and routing - which is very applicable to eCommerce, Aviation industry, Hotel industry, etc.
• I recently also saw a video about Volvo truck driver getting out of the truck in a difficult terrain, and walking in front of the truck, controlling its movement using a game-like controller
• Medical equipment / gadgets for preventive / alerting health-care products

Also, Dr. Ravi Mehrotra, from IDeaS made a very powerful statement in his keynote - that I loved!! 

He said - "Best way to learn, is to forget what is not important".

This statement resonates a lot with what I think .... one needs to forget what is not (as) important, in order to focus and prioritize on what is important and can add value.

Especially true for Testers to keep in mind!

ODSC - Data Science, AI, ML - Hype, or Reality?

I got a chance to attend ODSC India, held in Bangalore on 31st Aug / 1st Sept. For those who don't know, ODSC is the largest Applied Data Science and AI conference, and it was conducted in India the first time this year.

I was very excited to attend this for couple of reasons:

• I was attending a conference after a long time (i.e. where I was not speaking). So this was going to be a pure learning and knowing expedition for me.
• Data Science / AI / ML have become huge buzzwords in the industry now. I had some opinions about it - but that was with limited knowledge / understanding about it. I was hungry to learn some specific of these buzzwords.

Since I was going to travel to Bangalore for ODSC anyway, I also decided to participate in the pre-conference workshop - Advanced Data Analysis, Dashboards And Visualization. I thought it would be interesting to learn about the What, Why and How of the techniques of Data Analysis, Dashboards and visualization - which would help me as I rebuild / extend TTA (Test Trend Analyzer). Though the workshop was good, it focused completely on Tableau as a tool and unfortunately did not meet my objectives / expectations. That said, there is another tool I came across in the conference - KNIME - seems interesting and am going to try it out.

The conference was good though. I attended a lot of sessions and had lot of hallway-conversations with many interesting people. Typical outcome of attending a conference, some sessions I liked better than others, some were amazing, some were mediocre. 

Here is my unstructured assessment of what I now think about what I heard and discussed:

• Advanced mathematics learnt in colleges has an application in data science. So if children / kids ask why should they study Statistics - here is an answer!
• Creating data models without Business Context will not work. If it does, you have been lucky :)
• There are some interesting case studies and success stories of AI & ML. But these are the same success stories around since quite some time. All the other "noise" of AI & ML so far seems a hype so far.
• There is a lot of value in understanding historical data better. Based on that understanding, there can be opportunities to forecast the future. There is a huge risk of doing this forecasting, IF % of uncertainty is not included as part of it. However, it is very easily ignored.
• Understanding of Neural Networks, computing, and algorithms is essential to building intelligent solutions for complex problems.
• It is not sufficient to get better / accurate prediction results. Being able to explain how and why those results are better / same / worse is equally important. In many cases, this would be a regulatory requirement.
• Data Science is the "art" & "science" of understanding data better. To do this, we need to first cleanse / prep the data, simplify it using various techniques, and learn techniques to visualize the data.
• There is a "grammar of graphics" and a "grammar of interactive graphics" - which helps in thinking about data visualization.
• Deploying these AI / ML solutions to production is not a trivial task - mainly due to the fact of high computing and huge volume of data processing required to make it production ready. - This is a huge opportunity for the general Software Development / Testing/ DevOps community to solve problems faced by data scientists / people in the data science / AI / ML domain.
• With data privacy laws rightly becoming stricter, you need to be careful and use only legally obtained sample datasets for analyzing / training the data models - else there is going to be huge penalties for companies involved. (This is in reference to GDPR, a new law coming up in USA and also India.)
• Earlier, only PhD holder were qualified folks to work on Data Science. Now-a-days, the trend is to get relevant training to interns, and have them work on these problems, and then get the results validated / explained by the PhD specialists.
• In a nutshell - Data Science, AI, ML are using specialized types of tools and technologies to solve different problems. People / organizations have been doing these activities before the buzzwords were formed / or got popular.

So, what is my core takeaway from this? 

• As with any new buzzword, there is interesting work happening in Data Science, AI & ML - but the majority claiming to be in the field are just creating and riding the hype!

That said, I want to do the following:

• Find opportunities to investigate and understand the Data Science + AI + ML in more detail. 
• Understand the skills and capabilities required from a software developer + QA role perspective to contribute more effectively in solving these newer problem statements
• Learn python / R 
• Experiment with various tools / libraries related to data visualization