Of Byzantine Failures,unintended consequences & Architecture Heuristics

P.S: Copy of my blog in Linkedin

Way …. back in 2007, I gave a talk on Architecture Heuristics – we talked about Byzantine failures, systems with strong bones and the politics of systems architectures.

One would think that all this is way behind us ! Apparently not so ! There is a software bug in 787 GCU ! The root cause – yep you guessed it, integer overflow !

The plane’s electrical generators fall into a failsafe mode if kept continuously powered on for 248 days. The 787 has four such main generator-control units that, if powered on at the same time, could fail simultaneously and cause a complete electrical shutdown

And self-parking car hits pedestrians because …

Keeping the car safe is included as a standard feature, but keeping pedestrians safe isn’t. …

Interesting … whatever happened to the prime directive ? And Pedestrian Recognition – an option in self parking cars ? What next ? Steering wheel as an option ?

And, we keep on building machines that are software intensive ! Ford GT has more code than a 787 !

Back to Architecture Heuristics …

  1. Select technologies that you can dance with & Be flexible in scaling as you grow
  2. Embrace Failure & Influence Scalability
  3. Build systems with good bones (my slides from 2007 sill look relevant!)
  4. Solve the right problems
  5. While we build complex AI systems, remember that our ingenuity is hard to beat – even by the smart machines that we build !
  6. And, those who don’t learn from the history should read these recommendations, they are still valid !
  7. … Of course, pay that extra $3,000 and buy the Pedestrian Detection – you might drive the car in this world (where we humans reside – at least for now) not in Mars !

Take Care of the Ball, Value every Possession & Protect the Rim

P.S: Copy of my blog in Linkedin

CurryWas watching the NBA Western Conference Finals; the Warriors Team, Coach Kerr & Stephen Curry all are inspiration not only for Sports but also for the startup world.

I picked up a few insightful quotations from the post-game conference … will let you fill-in the inferences & lessons to keep this blog short …

Agility & Nimbleness : What I like most about the Warriors, is the way they morph & raise to the occasion. They find ways to reorganize & adapt against different teams … time will tell how they will do against LeBron and the Cleveland Gang … but for now, they are very effective …

[Update 5/24/15] Interestingly, today Tim Kawakami expressed the same sentiment in his blog at San Jose Mercury News !

“Take Care of the ball, value every possession & protect the rim” – Steve Kerr. Lot of truth in this statement … for life and business …

Steve Kerr about Harden “He sees every angle and we try to close as many of them as we can …”. That is all what we need to do in business to get ahead. The talented will make the shots, under any circumstances, like Kobe says … (er, tweets)

So be comfortable in taking those difficult shots !

Lesson for the Rockets : “Don’t play around the edges, play in the paint” echoed by Kevin McHale “Win the paint & win the board” … So true in sports and in startup business …

Curry Flurry : “Stephen Curry is very patient & will let the offense come to him ! Then he starts !” – In game 3 he had 40 points but only one in 1st quarter ! Once he got the offense, he flawlessly executed his characteristic “confidence & smoothness of the shots” …

In short, “Steph”, Kerr said “was Steph” !

BTW, don’t count the Houston Rockets out yet ! Against all odds, they won against LA Cilppers; Harden & Kevin McHale have a way with adversities …

And on another note, I need to update my NFL/ELO blog to applying ELO in BasketBall …

Reference for material & pictures:

  1. http://www.nytimes.com/2015/05/24/sports/basketball/stephen-curry-with-a-little-help-tramples-the-rockets.html
  2. http://scores.espn.go.com/nba/recap?gameId=400796357
  3. http://scores.espn.go.com/blog/statsinfo/post/_/id/105884/harden-swoons-curry-soars-in-game-3
  4. http://uproxx.com/dimemag/2015/05/kobe-bryant-stephen-curry-defense-twitter/
  5. http://diys.didiroesmana.com/trends/golden-state-warriors
  6. http://blogs.mercurynews.com/kawakami/2015/05/23/when-the-warriors-figure-out-an-opponent-this-happens-a-game-3-houston-obliteration-by-the-playoff-monster/

Data Science is the new Electronics

P.S: This is a copy of my blog in Linkedin.


A good friend of mine asked me “What exactly is this Data Science”?

That got me thinking – we have tons of blogs on “Who or What is a Data Scientist” including mine.

One can explain the intuition behind Data Science, the pragmas of the profession, but not the essence !

Then I remembered an engineer on a flight to Tokyo, who was at 61G, I was 61H. It was years ago, probably a lot more years than many (or most) of the readers would remember. I asked him what he was doing and his answer was “Helping companies to embed electronics in their products!”. I remember when autos had no electrical circuits except for the lights. Then came ignition electronics, engine electronics and now powerful computers that control almost all functions; except, of course, to roll where we still need old-fashioned wheels & tires !

We are at that stage with Data Science, where the three Amigos of Data Science(Intelligence, Inference and Interface) can be embedded in enterprise systems increasing their capabilities that far exceed the current ones !

We can really build adaptive systems .. not descriptive, not reactive but truly adaptive, that have malleable intelligence instead of the brittle newtonian rules !

As Sonny Elliot would say – Exactically!

Exactically similar to Electronics some years ago ! Now is the time to think Data Science as embeddable modules with Intelligence/Inference at the systems level and interesting Interfaces for the users …

And that, probably, is the mission of Data Scientists …

If they choose to accept … This blog could self-destruct in 5 seconds …5…4…3…2

Data Science with Spark on the Databricks Cloud – Training at SparkSummit (East)

DataSci-03-P24We had a good Data Science training session in Sheraton, Times Square, NY; second day of SparkSummit (East). It was my privilege to co-author and lead the Data Science track, along with Reza, Paco, Andy, Hossein, TD,Joseph and Xiangrui. I have shared the slideset at Slideshare as well as at the Databricks site.

[Update 4/12/15] : The video is posted at Youtube (5hrs!)

This was the second time I was involved with a training fully based off of the Databricks cloud and it worked out very well ! The Databricks cloud was very robust and resilient. Unfortunately we had problems with the wireless at the Sheraton Hotel !DataSci-03-P27
The training was a mixture of hands-on and lecture.We sterted out with a dataset of 30 records and then moved onto the titanic dataset (900) to the movielens medium (1,000,000) and finally with the RecSyschallenge dataset (33,000,000!). What a progression in a day !

You can see the details in the slides. Ping me if you have any questions.

DataSci-03-P28Data wrangling over the RecSysChallenge 2015 data captures the essence of the Databricks cloud. I will quickly cover the RecSys Challenge dataset as an illustration.

The training data consists of 33,003,944 clicks and 1,150,753 buys. Our mission, if we choose to accept is to predict the session-items bought from a test dataset of 8,251,791 clicks.

A quick data exploration workflowdbc-01:



All at scale, in an elastic cloud, seamlessly moving between dev, model, stage and prod ! The magic of Databricks Cloud !

BTW, we also explored the State Of the Union Speeches from Washington, Lincoln, FDR, Clinton, Bush & Obama. The graphs below show a succinct view of the mood of the nation at each periods …


And finally after 100 slides later …!


The Art of NFL Ranking, the ELO Algorithm and FiveThirtyEight

In this blog, I will focus on the NFL Ranking based on the ELO algorithm that Nate Silver’s FiveThirtyeight uses. The guys at 538 have done a good job.The ELO and NFL ranking was part of my workshop at the Global Big Data Conference this Sunday. The full presentation is in slideshare

ELO – the algorithm made famous by Facebook & depicted in the movie Social Network


 Basic ELO


The k-Factor is the main leverage point to customize the algorithm for different domains.

  • For example Chess has no notion of a season; Soccer,Football & Basket ball are dependent on seasons – teams change during different seasons
  • Chess has no score to consider except WIn,Lose or Draw; but ball games have scores that need to be accommodated
  • For Chess k=10; for soccer it varies from 20 to 60; 20 for friendly matches to 60 for World Cup Finals
  • As we will see later, NFL adjusts k with the Margin Of Victory Multiplier
  • NFL also adjusts k to weigh recent games more heavily, w/ exponential decay
  • There are also mechanisms for weighing playoffs higher than regular season games (We will see this in Basketball)

538’s take on ELO



NFL 2014 Predicts & Results

The R program ELO-538.R is in Github

2014 Ranking Table





To Do

  1. Exponential decay with more weight for recent games – later in the season
  2. Calculate the rankings from 1940 to present, draw graphs like this from 538

Augmented Cognitive Intelligence

Have been working on this architecture for a couple of years. The idea is to build an AI machine that augments the human capabilities. I know IBM has Watson; Google, FB all have their own versions that address different domains.

The diagram below is more for my understanding and to clarify the thinking. I will write more as I get time. Hope you all find it useful.