Google Cloud is finally here

Amazon gets some competition, and customers should see some relief, unless Google withdraws commitment on these products after a few years of trying (like it often does now!)

Machine Type Pricing
Configuration Virtual Cores Memory GCEU * Local disk Price/Hour $/GCEU/hour
n1-standard-1-d 1 3.75GB *** 2.75 420GB *** $0.145 0.053
n1-standard-2-d 2 7.5GB 5.5 870GB $0.29 0.053
n1-standard-4-d 4 15GB 11 1770GB $0.58 0.053
n1-standard-8-d 8 30GB 22 2 x 1770GB $1.16 0.053
Network Pricing
Ingress Free
Egress to the same Zone. Free
Egress to a different Cloud service within the same Region. Free
Egress to a different Zone in the same Region (per GB) $0.01
Egress to a different Region within the US $0.01 ****
Inter-continental Egress At Internet Egress Rate
Internet Egress (Americas/EMEA destination) per GB
0-1 TB in a month $0.12
1-10 TB $0.11
10+ TB $0.08
Internet Egress (APAC destination) per GB
0-1 TB in a month $0.21
1-10 TB $0.18
10+ TB $0.15
Persistent Disk Pricing
Provisioned space $0.10 GB/month
Snapshot storage** $0.125 GB/month
IO Operations $0.10 per million
IP Address Pricing
Static IP address (assigned but unused) $0.01 per hour
Ephemeral IP address (attached to instance) Free
* GCEU is Google Compute Engine Unit — a measure of computational power of our instances based on industry benchmarks; review the GCEU definition for more information
** coming soon
*** 1GB is defined as 2^30 bytes
**** promotional pricing; eventually will be charged at internet download rates

Google Prediction API

Tap into Google’s machine learning algorithms to analyze data and predict future outcomes.

Leverage machine learning without the complexity
Use the familiar RESTful interface
Enter input in any format – numeric or text

Build smart apps

Learn how you can use Prediction API to build customer sentiment analysis, spam detection or document and email classification.

Google Translation API

Use Google Translate API to build multilingual apps and programmatically translate text in your webpage or application.

Translate text into other languages programmatically
Use the familiar RESTful interface
Take advantage of Google’s powerful translation algorithms

Build multilingual apps

Learn how you can use Translate API to build apps that can programmatically translate text in your applications or websites.

Google BigQuery

Analyze Big Data in the cloud using SQL and get real-time business insights in seconds using Google BigQuery. Use a fully-managed data analysis service with no servers to install or maintain.

Reliable & Secure

Complete peace of mind as your data is automatically replicated across multiple sites and secured using access control lists.
Scale infinitely

You can store up to hundreds of terabytes, paying only for what you use.
Blazing fast

Run ad hoc SQL queries on
multi-terabyte datasets in seconds.

Google App Engine

Create apps on Google’s platform that are easy to manage and scale. Benefit from the same systems and infrastructure that power Google’s applications.

Focus on your apps

Let us worry about the underlying infrastructure and systems.
Scale infinitely

See your applications scale seamlessly from hundreds to millions of users.
Business ready

Premium paid support and 99.95% SLA for business users

Possible Digital Disruptions by Cyber Actors in USA Electoral Cycle

Some possible electronic disruptions  that threaten to disrupt the electoral cycle in United States of America currently underway is-

1) Limited Denial of Service Attacks (like for 5-8 minutes) on fund raising websites, trying to fly under the radar of network administrators to deny the targeted  fundraising website for a small percentage of funds . Money remains critical to the world’s most expensive political market. Even a 5% dropdown in online fund-raising capacity can cripple a candidate.

2)  Limited Man of the Middle  Attacks on ground volunteers to disrupt ,intercept and manipulate communication flows. Basically cyber attacks at vulnerable ground volunteers in critical counties /battleground /swing states (like Florida)

3) Electro-Magnetic Disruptions of Electronic Voting Machines in critical counties /swing states (like Florida) to either disrupt, manipulate or create an impression that some manipulation has been done.

4) Use search engine flooding (for search engine de-optimization of rival candidates keywords), and social media flooding for disrupting the listening capabilities of sentiment analysis.

5) Selected leaks (including using digital means to create authetntic, fake or edited collateral) timed to embarrass rivals or influence voters , this can be geo-coded and mass deployed.

6) using Internet communications to selectively spam or influence independent or opinionated voters through emails, short messaging service , chat channels, social media.

7) Disrupt the Hillary for President 2016 campaign by Anonymous-Wikileak sympathetic hacktivists.



Book Review- Machine Learning for Hackers

This is review of the fashionably named book Machine Learning for Hackers by Drew Conway and John Myles White (O’Reilly ). The book is about hacking code in R.


The preface introduces the reader to the authors conception of what machine learning and hacking is all about. If the name of the book was machine learning for business analytsts or data miners, I am sure the content would have been unchanged though the popularity (and ambiguity) of the word hacker can often substitute for its usefulness. Indeed the many wise and learned Professors of statistics departments through out the civilized world would be mildly surprised and bemused by their day to day activities as hacking or teaching hackers. The book follows a case study and example based approach and uses the GGPLOT2 package within R programming almost to the point of ignoring any other native graphics system based in R. It can be quite useful for the aspiring reader who wishes to understand and join the booming market for skilled talent in statistical computing.

Chapter 1 has a very useful set of functions for data cleansing and formatting. It walks you through the basics of formatting based on dates and conditions, missing value and outlier treatment and using ggplot package in R for graphical analysis. The case study used is an Infochimps dataset with 60,000 recordings of UFO sightings. The case study is lucid, and done at a extremely helpful pace illustrating the powerful and flexible nature of R functions that can be used for data cleansing.The chapter mentions text editors and IDEs but fails to list them in a tabular format, while listing several other tables like Packages used in the book. It also jumps straight from installation instructions to functions in R without getting into the various kinds of data types within R or specifying where these can be referenced from. It thus assumes a higher level of basic programming understanding for the reader than the average R book.

Chapter 2 discusses data exploration, and has a very clear set of diagrams that explain the various data summary operations that are performed routinely. This is an innovative approach and will help students or newcomers to the field of data analysis. It introduces the reader to type determination functions, as well different kinds of encoding. The introduction to creating functions is quite elegant and simple , and numerical summary methods are explained adequately. While the chapter explains data exploration with the help of various histogram options in ggplot2 , it fails to create a more generic framework for data exploration or rules to assist the reader in visual data exploration in non standard data situations. While the examples are very helpful for a reader , there needs to be slightly more depth to step out of the example and into a framework for visual data exploration (or references for the same). A couple of case studies however elaborately explained cannot do justice to the vast field of data exploration and especially visual data exploration.

Chapter 3 discussed binary classification for the specific purpose for spam filtering using a dataset from SpamAssassin. It introduces the reader to the naïve Bayes classifier and the principles of text mining suing the tm package in R. Some of the example codes could have been better commented for easier readability in the book. Overall it is quite a easy tutorial for creating a naïve Bayes classifier even for beginners.

Chapter 4 discusses the issues in importance ranking and creating recommendation systems specifically in the case of ordering email messages into important and not important. It introduces the useful grepl, gsub, strsplit, strptime ,difftime and strtrim functions for parsing data. The chapter further introduces the reader to the concept of log (and affine) transformations in a lucid and clear way that can help even beginners learn this powerful transformation concept. Again the coding within this chapter is sparsely commented which can cause difficulties to people not used to learn reams of code. ( it may have been part of the code attached with the book, but I am reading an electronic book and I did not find an easy way to go back and forth between the code and the book). The readability of the chapters would be further enhanced by the use of flow charts explaining the path and process followed than overtly verbose textual descriptions running into multiple pages. The chapters are quite clearly written, but a helpful visual summary can help in both revising the concepts and elucidate the approach taken further.A suggestion for the authors could be to compile the list of useful functions they introduce in this book as a sort of reference card (or Ref Card) for R Hackers or atleast have a chapter wise summary of functions, datasets and packages used.

Chapter 5 discusses linear regression , and it is a surprising and not very good explanation of regression theory in the introduction to regression. However the chapter makes up in practical example what it oversimplifies in theory. The chapter on regression is not the finest chapter written in this otherwise excellent book. Part of this is because of relative lack of organization- correlation is explained after linear regression is explained. Once again the lack of a function summary and a process flow diagram hinders readability and a separate section on regression metrics that help make a regression result good or not so good could be a welcome addition. Functions introduced include lm.

Chapter 6 showcases Generalized Additive Model (GAM) and Polynomial Regression, including an introduction to singularity and of over-fitting. Functions included in this chapter are transform, and poly while the package glmnet is also used here. The chapter also introduces the reader formally to the concept of cross validation (though examples of cross validation had been introduced in earlier chapters) and regularization. Logistic regression is also introduced at the end in this chapter.

Chapter 7 is about optimization. It describes error metric in a very easy to understand way. It creates a grid by using nested loops for various values of intercept and slope of a regression equation and computing the sum of square of errors. It then describes the optim function in detail including how it works and it’s various parameters. It introduces the curve function. The chapter then describes ridge regression including definition and hyperparameter lamda. The use of optim function to optimize the error in regression is useful learning for the aspiring hacker. Lastly it describes a case study of breaking codes using the simplistic Caesar cipher, a lexical database and the Metropolis method. Functions introduced in this chapter include .Machine$double.eps .

Chapter 8 deals with Principal Component Analysis and unsupervised learning. It uses the ymd function from lubridate package to convert string to date objects, and the cast function from reshape package to further manipulate the structure of data. Using the princomp functions enables PCA in R.The case study creates a stock market index and compares the results with the Dow Jones index.

Chapter 9 deals with Multidimensional Scaling as well as clustering US senators on the basis of similarity in voting records on legislation .It showcases matrix multiplication using %*% and also the dist function to compute distance matrix.

Chapter 10 has the subject of K Nearest Neighbors for recommendation systems. Packages used include class ,reshape and and functions used include cor, function and log. It also demonstrates creating a custom kNN function for calculating Euclidean distance between center of centroids and data. The case study used is the R package recommendation contest on Kaggle. Overall a simplistic introduction to creating a recommendation system using K nearest neighbors, without getting into any of the prepackaged packages within R that deal with association analysis , clustering or recommendation systems.

Chapter 11 introduces the reader to social network analysis (and elements of graph theory) using the example of Erdos Number as an interesting example of social networks of mathematicians. The example of Social Graph API by Google for hacking are quite new and intriguing (though a bit obsolete by changes, and should be rectified in either the errata or next edition) . However there exists packages within R that should be atleast referenced or used within this chapter (like TwitteR package that use the Twitter API and ROauth package for other social networks). Packages used within this chapter include Rcurl, RJSONIO, and igraph packages of R and functions used include rbind and ifelse. It also introduces the reader to the advanced software Gephi. The last example is to build a recommendation engine for whom to follow in Twitter using R.

Chapter 12 is about model comparison and introduces the concept of Support Vector Machines. It uses the package e1071 and shows the svm function. It also introduces the concept of tuning hyper parameters within default algorithms . A small problem in understanding the concepts is the misalignment of diagram pages with the relevant code. It lastly concludes with using mean square error as a method for comparing models built with different algorithms.


Overall the book is a welcome addition in the library of books based on R programming language, and the refreshing nature of the flow of material and the practicality of it’s case studies make this a recommended addition to both academic and corporate business analysts trying to derive insights by hacking lots of heterogeneous data.

Have a look for yourself at-

Creating Pages on Google Plus for some languages

So I decided to create Pages on Google Plus for my favorite programming languages.

a programming language that lets you work more quickly and integrate your systems more effectively

Add to circles

  –  Comment  –  Share

Ajay Ohri

Ajay Ohri's profile photo

Ajay Ohri  –
Ajay Ohri shared a Google+ page with you.
Structured Query Language
Leading statistical language since 1960’s especially in sociology and market research
The leading statistical language in the world
The leading statistical language since 1970’s



These are in accordance with Google’s Policies  Continue reading “Creating Pages on Google Plus for some languages”

Review of Google Plus

After resisting for two weeks I have decided to write a Google Plus review. This includes both the changed designed parameters, the invite growth features and all of the main sub-items and activities you can do in the G+  Stream, Share, Hang Out, Pictures, Circles.

Since I have 2500 people in my circles and I am in 91 circles

To keep it simple – I have noted the following 6 main sub-points.

1) Content Dissemination-


  • Sharing Blog Articles
  • Micro-Blogging
  • Sharing Pictures

2) Online Professional Networking  and 3) Online Personal Socializing

4) Spam Control / Malware /Phishing/Porn Protection

5) Time Cost versus Networking Benefit


1) Content Dissemination-

  • Sharing Blog Articles


Sharing is as simple as Facebook but the design makes it simpler.

Note G+ uses lower number of colors, bigger fonts, slightly bigger icons to reduce the appearance of clutter.

Contrast this

with this-


Interesting to see that G+ has four types of media to share- besides writing the status/micro-blog (unfettered by 140 characters). Note these show icons only with hover text to tell you what the icon stands for.

Photo,Video,URL,Location (which seems to be Twitter like in every share)

Facebook has 5 types of Sharing and note the slightly different order as well the fact that both icon and text make it slightly more cluttered- Status (which is redundant clearly ),Photo,Link,Video,Question

G+ thus lacks polls /questions features. It is much easier to share content on Facebook automatically as of now- but for G+ you need to share the URL privately though. There exist G+ meme-s already thanks to re-sharing in G+ plus which seems to be inspired by Tumblr (?).

In addition Google has made your Google Profile the number one SERP for searching your name, so there seem clear tied in benefits of SEO with content disseminated here.

G+ has sharing in circles whereas Facebook has only Everyone, Friends, Friends of Friends ,Customize.  This makes G+ interface slightly better in tweaking the spread of content to targeted audience esp by Bloggers.

  • For sharing Photos– G+ goes in for a whole new separate tab (one out of four) whereas Facebook treats photo sharing less prominently.
  • Google has lesser white space between photos, (The Facebook way used to be just snap photo by iPhone and send by email to auto-post), and the privacy in sharing photos is much better in G+ as the dropdowns in Facebook are not as granular and neither as nifty in icon design.
  • Also I like the hover and photo grows bigger feature and the auto import from Picassa (but I would like to auto-import into G+ from Flickr just as I can do in Facebook)
  • Google Plus also has a much more detailed version for sharing videos than photos as compared to Facebook  -upload Photo options  versus
  • G+ has much more focus on auto-sharing from mobiles




2) Online Professional Networking  and 3) Online Personal Socializing Organizing Contacts in Google Plus and seperate privacy controls make it easier to customize sharing without getting too complex. You can make as many circles and drag and drop very easily instead of manually clicking a dropdown box. Effectively speaking Facebook has just 4 kinds of circles and it does not distinguish between various types of friends which is great from philosophical point of view but not so goodn enforcing separateness between professional and personal networks. Note Facebook privacy settings are overwhelming despite the groovy data viz

4) Spam Control / Malware /Phishing/Porn Protection 

Spam Control in Facebook versus in Google Plus- note the different options in Google Plus (including the ability to NOT reshare). I am not aware of more enhanced protection than the ones available for Gmail already. Spam is what really killed off a lot many social networks and the ability to control or reduce spam will be a critical design choice

5) Time Cost versus Networking Benefit

Linkedin has the lowest cost in time spent and networking done. If G+ adds a resume section for jobs, recruiters, and adds in Zynga games, the benefit from G+ will expand. As of now G+ is a minimal social network with minimalism as design ethos.

(Zynga would do well to partner with G+)


Topic Models in R- search documents for similarity by frequency

Image via Wikipedia

From the marvelous lovely Journal of Statistical Software, ignored by mainstream corporatia, but beloved to academia. here is one more interesting and very timely paper.

Can be used to grade stdudents homework, catch terrorists as in plagiarists , search engine spam linkers. Enjoy!