Explaining #Containers / #Kubernetes to a Child : How To Become a Storytelling Steward Using Gamification & Graphic Novels (Comics)

How To Become a Storytelling Steward Using Gamification & Graphic Novels (Comics)

If someone asked you to explain the benefits of #Containers / #Kubernetes as though you were speaking to a child, do it in under 2 minutes & guarantee said kiddo’s comprehension, could you do it?

I was tasked recently with doing the same thing by my COO using Machine Learning as the main talking point . To elucidate, I decided to pose the same question to several peers who graciously entertained this whim ‘o mine. While they provided technically correct explanations’, often, they parked their response somewhere between boredom-block and theoretical thoroughfare. Yawn!

What those very intelligent practitioners failed to remember was this was NOT the latest round of stump-the-chump; that the goal was to explain Machine Learning in a way that a child could grok without “adult-splaining” / “grownup-eze” or other explanatory methods – Add to this, the goal of keeping the child engaged for the full 2 minutes –> well, shoot,  ++ to you, dear reader turned supreme storytelling savant if you made that happen. While we are at it, why not add the ability to explain ML while avoiding the dreaded “eye gloss over” affect that most listeners dawn when tuning out their brain. This ‘Charlie Brown’ <whoah whoah whoah> adult vernacular riposte is nearly always reflected back to the speaker via those truth telling eyes of yours, enabling the Edgar Allen Poe in us all. Huh? What I mean is the tell-tale Pavlovian heart response to any “Data Science-based” summarization, in my experience.

Instead, I described two scenarios involving exotic fruit –> the 1st, included the name for each of those curious fruits aka labels which was the basis for her being able to label them on demand accordingly. The 2nd scenario,  also involved exotic fruits BUT the difference was that she was NOT provided any names ahead of time yet still was tasked with naming said items –  And for those data scientists reading this, naturally, were metaphors for supervised and unsupervised learning.

Originally, I had prepared a similar talk for a ML centric presentation I was set to give to a community based data science event (later shared on this blog) – It contains about 90% comics / image iconography instead of laborious text per slide & was received incredibly well. In fact, when delivered to a 200+ audience, it was met with much applause and higher than normal attendee survey satisfaction scores. Simplicity & pacing; Remember, an image speaks 1000 words and to a child who often learns experientially / visually, well, it becomes the storytellers handbook to the hive mind of children everywhere :).

By the way, you can stop me next time when I diverge so sharply from the path.

But now we are back –> putting to bed my thorough digression & pulling you back to my 1st sentence above:

If someone asked you to explain the benefits of #Containers / #Kubernetes as though you were speaking to a child, do it in under 2 minutes & guarantee said kiddo’s comprehension, could you do it?

This awesome comic/bedtime story is one way to answer with a resounding YES ! Meet Phippy & Zee and follow their adventures as they head off to the Zoo: Phippy Goes To The Zoo_A_Kubernetes_Story: https://azure.microsoft.com/en-us/resources/phippy-goes-to-the-zoo/en-us/

Eye Tracking & Applied ML: Soapbox Validations

Anyone who has read my blog (shameless self-plug: http://www.lauraedell.com) over the past 12 years will know, I am very passionate about drinking my own analytical cool-aid. Whether during my stints as a Programmer, BI Developer, BI Manager, Practice Lead / Consultant or Senior Data Scientist, I believe wholeheartedly in measuring my own success with advanced analytics.  Even my fantasy football success (more on that in a later post) can be attributed to Advanced Machine Learning…But you wouldn’t believe how often this type of measurement gets ignored.
eyetracking
Introducing you, dear reader, to my friend “Eye-Tracker” (ET). Daunting little set of machines in that image, right?! But ET is a bonafide bada$$ in the world of measurement systems; oh yeah, and ET isn’t a new tech trend – in fact, mainstream  ET systems are a staple of any PR, marketing or web designers’ tool  arsenal  as a stick to measure program efficacy between user intended behavior & actual outcomes/actions.

In my early 20’s, I had my own ET experience & have been a passionate advocate since, having witnessed what happens when you compound user inexperience with poorly designed search / e-commerce operator sites.  I was lucky enough to work for the now uber online travel company who shall go nameless (okay, here is a hint: remember a little ditty that ended with some hillbilly singing “dot commmm” & you will know to whom I refer). This company believed so wholeheartedly in the user experience that they allowed me, young ingénue of the workplace, to spend thousands on eye tracking studies against a series of balanced scorecards that I was developing for the senior leadership team. This is important because you can ASK someone whether a designed visualization is WHAT THEY WERE THINKING or WANTING, even if built iteratively with the requestor. Why, you ponder to yourself, would this be necessary when I can just ask/survey my customers about their online experiences with my company and saved beaucorp $$.

Well, here’s why: 9x out of 10, survey participants, in not wanting to offend, will nod ‘yes’  instead of being honest, employing conflict avoidance at its best. Note, this applies to most, but I can think of a few in my new role who are probably reading this and shaking their head in disagreement at this very moment.

Eye tracking studies are used to measure efficacy by tracking what content areas engage users’ brains vs. areas that fall flat, are lackluster, overdesigned &/or contribute to eye/brain fatigue. It measures this by “tracking” where & for how long your eyes dwell on a quadrant (aka visual / website content / widget on a dashboard) and by recording the path & movement of the eyes between different quadrants’ on a page. It’s amazing to watch these advanced, algorithmic-tuned systems, pick up even the smallest flick of one’s eyes, whether darting to or away from the “above-fold” content, in ‘near’ real-time. The intended audience being measured generates the validation statistics necessary to evaluate how well your model fit the data. In the real-world, receiving attaboys or “ya done a good job” high fives should be doled out only after validating efficacy: eg. if customers dwell time increases, you can determine randomness vs. intended actual; otherwise, go back to the proverbial drawing board until earn that ‘Atta boy’ outright.

What I also learned which seems a no-brainer now; people read from Left Top to Right Bottom (LURB). So, when I see anything that doesn’t at LEAST follow those two simple principles, I just shake my head and tisk tisk tisk, wondering if human evolution is shifting with our digital transformation journey or are we destined to be bucketed with the “that’s interesting to view once” crowd instead of raising to the levels of usefulness it was designed for.

Come on now, how hard is it to remember to stick the most important info in that top left quadrant and the least important in the bottom right, especially when creating visualizations for use in the corporate workplace by senior execs. They have even less time & attention these days to focus on even the most relevant KPIs, those they need to monitor to run their business & will get asked to update the CEO on each QTR, with all those fun distractions that come with the latest vernacular du-jour taking up all their brain space: “give me MACHINE LEARNING or give me death; the upstart that replaced mobile/cloud/big data/business intelligence (you fill in the blank).
But for so long, it was me against the hard reality that no one knew what I was blabbing on about, nor would they give me carte blanche to re-run those studies ever again , And lo and behold, my Laura-ism soapbox has now been vetted, in fact, quantified by a prestigious University professor from Carnegie, all possible because a little know hero named Edmond Huey, now near and dear to my heart, grandfather of the heatmap, followed up his color-friendly block chart by building the first device capable of tracking eye movements while people were reading. This breakthrough initiated a revolution for scientists but it was intrusive and readers had to wear special lenses with a tiny opening and a pointer attached to it like the 1st image pictured above.
Fast forward 100 years…combine all ingredients into the cauldron of innovation & technological advancement, sprinkled with my favorite algorithmic pals: CNN & LSTM & voila! You have just baked yourself a popular visualization known as a heat/tree map (with identifiable info redacted) :
This common visual is  akin to eye tracking analytics which you will see exemplified in the last example below. Cool history lesson, right?

Even cooler is this example from a travel website ‘Travel Tripper’ which published Google eye-tracking results specific to the hotel industry. Instead of a treemap that you might be used to (akin to a Tableau or other BI tool visualization OOTB), you get the same coordinates laid out over search results in this example; imagine having your website underneath and instead of guessing what content should be above or below the fold, in the top left or right of the page, you can use these tried and true eye tracking methods to quantify exactly what content items customers or users are attracted to 1st and where their eyes “dwell” the longest on the page (red hot).

So, for those non-believers, I say, become a web analytic trendsetter, driving the future of machine design forward (ala “Web Analytics 3.0”).

Be a future-thinker, forward mover, innovator of your data science sphere of influence, always curious yet informed to make intelligent choices.