AWS EC2 Price worksheet


It all started with a tweet Image

  • It so happens that I have been working on a similar worksheet for pricing & configuring our analytics infrastructure;
  • I modified the one I am working on (inspired by the original at ec2 pricing_and_capacity) & morphed into the one Otis wanted
  • The Excel worksheet is hosted it in github. Feel free to modify it to fit your needs. Let me know as well …
  • I have four sets of prices viz. on-demand, reserved-light,reserved-medium usage and reserved- heavy usage. The prices are calculated for one year (8640 hrs) off of the cell M1 – one has to prorate the upfront fees to get the effective $/hr rate
  • The worksheet has multiple uses – I use it to compute the price difference for different usage patterns-high memory for Spark, different sizes for HBase cluster et al. As it is a spreadsheet one could sort it on varying criteria; one could change the numbers (say 6 months) and see what model makes sense.
  • BTW, it is interesting to see that the Light -Reserved costs more in all cases except for the storage models.
  • Long time ago, I had a graphical representation, which has become very dated. I might resurrect it with the new prices …

The Spreadsheet :

The left columns have the attributes of the various EC2 models.

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The 8640 (hrs/year) is in M1. All the calculations are based on this cell. The reserved light is interesting … it costs more !

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The reserved medium does save $. Moreover, one can stop the instances when not needed.

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I have calculated the yearly price prorating the upfront fees et al. But for Heavy Reserved, it is somewhat meaningless as they will charge for the whole year even if the instances are stopped. But changing the value in M1 gives a feel for the different tiers …

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I would be happy to hear other inferences we can make and add columns to the worksheet …

Cheers

 

The Chronicles of Robotics at First Lego League – Day 1


This week am at St.Louis, volunteering at the First lego league World Robotics Competition. Have been involved with First Robotics since 2004. Usually my position is Robotic Design Judge – a front seat view to interesting & innovative ideas on Robots.

For 3 days we have the Edwards Jones Dome & the America Center in St.Louis, MO.

Day 1 : Stardate : 91913.81

Judges’ on-site meeting & briefing, allocations & FLL opening ceremonies.

Some quick pictures … Full day of judging starts tomorrow early morning  … looking forward to it ….

  • View From my Hotel

  • FLL-01 FLL-02 RoomView-01RoomView-02
  • The Trophies

  • awards-01 awards-02
  • The Field 

  • The field occupies the stadium. It consists of six areas – Einstein(FLL), Geleleo, Franklin, Newton, Edison & Curie. I have tried to capture the view of the fields from the ground and from the bleachers.
  • Galeleo

  • Field-01-01 Field-01-02 Field-03 Field-04-01Field-04Field-01 Field-05
  • NASA Truck to beam the competitions live via satellite

  • Field-06 Field-07
  • Franklin & Newton

  • Field-02-01 Field-02 Fig-05-01 Field-08 Field-09
  • Einstein (FLL and the venue of opening ceremonies – below)

  • Field-10 Field-11
  • FLL Opening Ceremonies

  • Open-01 Open-02 Open-03-01 Open-03 Open-04
  • Tomorrow is a busy day – robot judging whole day. Might not get time to take pictures
  • Still have to cover the pits, convention center hall filled with team stalls et al. One has to be there to understand the scale and the energy !

Data Science Folk Knowledge & Words of Wisdom


I have collected some words of wisdom on Data Science & Machine Learning for my pycon 2014 Tutorial “Data Wrangling”.
Posting as a blog. The pdf is at Slideshare. Would appreciate comments, insights & corrections.

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  • Relevant Papers To Read

  • Slide22
  • An ordered list of mooc links & books. This was my answer in Quora

  • Slide23

A Glimpse of Google, NASA & Peter Norvig + The Restaurant at the End of the Universe


I came across an interesting talk by Google’s Peter Norvig at NASA.

Of course, you should listen to the talk – let me blog about a couple of points that are of interest to me:

Algorithms that get better with Data

Peter had two good points:

Norvig-01

  • Algorithms behave differently as they churn thru more data. For example in the figure, the Blue algorithm was better with a million training dataset. If one had stopped at that scale, one would be tempted to optimize that algorithm for better performance
  • But as the scale increased, the purple algorithm started showing promise – in fact the blue one starts deteriorating at larger scale. The old adage “don’t do premature optimization” is true here as well. 
  • Norvig-02In general, Google prefers algorithms that get better with data. Not all algorithms are like that, but Google likes to go after the ones with this type of performance characteristic. 

There is no serendipity in Google Search or Google Translate

  • There is no serendipity in search – it is just rehashing. It is good for finding things, but not at all useful for understanding, interpolation & ultimately inference. I think Intelligent Search is an oxymoron ;o)
  • Same with Google Translate. Google Translate takes all it’s cue from the web – it wouldn’t help us communicate with either the non-human inhabitants of this planet or any life form from other planets/milky ways.
    • In that sense, I am a little disappointed with Google’s Translation Engines.  OTOH, I have only a minuscule view of the work at Google.

The future of human-machine & Augmented Cognition

And, don’t belong to the B-Ark !

The Curious Case of the Data Scientist Profession


Data Science & the profession of a Data Scientist is being debated, rationalized, defined and refactored … I think the domain & the profession is maturing and our understanding of the Mythical Data Scientist is getting more pragmatic. Earlier, I had proposed the idea of a Data Science Engineer last year with similar thoughts; and elaborated more at “Who or what is a Data Scientist?“, “Building a Data Organization that works with Business” & “The sense & sensibility of Data Science devOps“. Things are getting more interesting …

Now to the highlights:

1. Data Scientist is multi-faceted & contextual

  • Two points – It requires a multitude of skills & different skill sets at different situations; and definitely is a team effort.
  • This tweet sums it all
  • DataScienceTeam
  • Sometimes a Data Scientist has to tell a good business story to make an impact; other times the algorithm wins the day
    • Harlan in his blog identifies four combinations – Data Business Person, Data Creative, Data Engineer & Data Researcher
      • I don’t fully agree with the diagram – it has lot less programming & little more math; math is usually built-in the ML algorithms and the implementation is embedded in math libraries developed by the optimization specialists. A Data Scientist should n’t be twiddling with the math libraries
    • The BAH Field Guide suggests the following mix:
    • Data Scienc 03
    • I would prefer to see more ML than M. ML is the higher from of applied M and also includes Statistics
  • Domain Expertise and the ability to identify the correct problems are very important skills of a Data Scientist, says John Forman.
  • Or as Rachel Schutt at Columbia quotes:
    • Josh Wills (Cloudera)
      • Data Scientist (noun): Person who is better at statistics than any software engineer & better at software engineering than any statistician

    • Will Cukierski (Kaggle) retorts
      • Data Scientist (noun): Person who is worse at statistics than any statistician & worse at software engineering than any software engineer

2. The Data Scientist team should be building data products

3.  To tell the data story effectively, the supporting cast is essential

  • As Vishal puts it in his blog,
    • Data must be there & processable – the story definitely depends on the data
    • Processes & buy-in from management – many times, it is not the inference that is the bottle neck but the business processes that needs to be changed to implement the inferences & insights
    • As the BAH Field Guide says it:
    • Data Scienc 04
    • DS01

 4.  Pay attention to how the Data Science team is organized

5. Data Science is a continuum of Sophistication & Maturity – a marathon than a spirint

Let me stop here, I think the blog is getting long already …

Is it still “Artificial” Intelligence, if our Computers learn -to think- from the workings of our Brain ?


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  • In fact that would be Natural Intelligence ! Intelligence is intelligence – it is a way of processing information to arrive at inferences, recommendations, predictions and so forth …

May be it is that Contemporary AI is actually just NI !

Point #1 : Machines are thinking like humans rather than acting like Humans

  • Primitives inspired by Computational Neuroscience like DeepLearning are becoming mainstream. We are no more enamored with Expert Systems that learn the rules & replace humans. We would rather have our machines help us chug through the huge amount of data.

We would rather interact with them via Google Glass – a two-way, highly interactive medium that act as a sensor array as well as augment cognition with a digital overlay over the real world

  • In fact, till now, our computers were mere brutes, without the elegance and finesse of the human touch !
  • Now the computers are diverging from Newtonian determinism to probabilistic generative models.
  • Instead of using greedy algorithms, the machines are now being introduced to Genetic Algorithms & Simulated Annealing. They now realize that local minima, computed via exhaustive brute force, are not the answers for all problems.
  • They now have knowledge graphs and have the capability to infer based on graph traversals and associated logic

Of course, deterministic transactional systems have their important place – we don’t want a probabilistic bank balance!

Point #2 : We don’t even want our machines to be like us

  • The operative word is “Augmented Cognition” – our machines should help us where we are not strong and augment our capabilities. More later …
  • Taking a cue from the contemporary media, “Person Of Interest” is a better model than “I,Robot” or “Almost Human” – a Mr.Spock, rather than a Sonny; Logical but resorts to the improbable and the random, when the impossible has been eliminated !

Point #3 : Now we are able to separate Interface from Inference & Intelligence

AI-03

  • New Yorker asks, “Why can’t my computer understand me?” Finding answers to questions like “Can an alligator run the hundred-meter hurdles?” is syntax.
  • NLP (Natural Language Processing) and it’s first cousin NLU(Natural Language Understanding) are not intelligence, they are interface.
  • In fact, the team that built IBM Watson realized that “they didn’t need a genius, … but build the world’s most impressive dilettante … battling the efficient human mind with spectacular flamboyant inefficiency”.

Taking this line of thought to it’s extreme, one can argue that Google (Search) itself is the case and point of an ostentatious and elaborate infrastructure for what it does … no intelligence whatsoever – Artificial or Natural ! It should have been based on knowledge graph rather than a referral graph. Of course, in a few years, they would have made huge progress, no doubt.

  • BTW, Stephen Baker has captured the “Philosophy of an Intelligent Machine” very well.
  • I have been & am keeping track of the progress by Watson.
  • Since then, IBM Watson. itself, has made rapid progress in the areas of Knowledge Traversal & Contextual Probabilistic Inferences i.e. ingest large volume of unstructured data/knowledge & reason about it
  • I am not trivializing the effort and the significance of machines to understand the nuances of human interactions (speech, sarcasm, slang, irony, humor, satire et al); but we need to realize that, that is not an indication of intelligence or a measure what machines can do.

Human Interface is not Human Intelligence, same with machines. They need not look like us, walk like us, or even talk like us. They just need to augment us where we are not strong … with the right interface, of course

  • Gary Markus in New Yorker article “Can Super Mario Save AI” says “Human brains are remarkably inefficient in some key ways: our memories are lousy; our grasp of logic is shallow, and our capacity to do arithmetic is dismal. Our collective cognitive shortcomings are so numerous … And yet, in some ways, we continue to far outstrip the very silicon-based computers that so thoroughly kick our carbon-based behinds in arithmetic, logic, and memory …

Well said Gary. Humans & Machines should learn from the other and complement … not mimic each other … And there is nothing Artificial about it …

I really wish we take “Artificial” out of AI – Just incorporate what we are learning about ourselves into our computers & leave it at that !

Finally:

AI-04-01