Writing on APIs for Programmable Web

I have been writing free lance on APIs for Programmable Web. Here is an updated list of the articles, many of these would be of interest to analytics users. Note- some of these are interviews and they are in bold. Note to regular readers: I keep updating this list , and at each updation bring it to the front page, then allowing the blog postings to slide it down!

Scoreoid Aims to Gamify the World Using APIs January 27th, 2014

Plot.ly’s Plot to Visualize More Data January 22nd, 2014

LumenData’s Acquisition of Algorithms.io is a Win-Win January 8th, 2014

Yactraq API Sees Huge Growth in 2013  January 6th, 2014

Scrape.it Describes a Better Way to Extract Data December 20th, 2013

Exclusive Interview: App Store Analytics API December 4th, 2013

APIs Enter 3d Printing Industry November 29th, 2013

PW Interview: José Luis Martinez of Textalytics November 6th, 2013

PW Interview Simon Chan PredictionIO November 5th, 2013

PW Interview: Scott Gimpel Founder and CEO FantasyData.com October 23rd, 2013

PW Interview Brandon Levy, cofounder and CEO of Stitch Labs October 8th, 2013

PW Interview: Jolo Balbin Co-Founder Text Teaser  September 18th, 2013

PW Interview:Bob Bickel CoFounder Redline13 July 29th, 2013

PW Interview : Brandon Wirtz CTO Stremor.com   July 4th, 2013

PW Interview: Andy Bartley, CEO Algorithms.io  June 4th, 2013

PW Interview: Francisco J Martin, CEO BigML.com 2013/05/30

PW Interview: Tal Rotbart Founder- CTO, SpringSense 2013/05/28

PW Interview: Jeh Daruwala CEO Yactraq API, Behavorial Targeting for videos 2013/05/13

PW Interview: Michael Schonfeld of Dwolla API on Innovation Meeting the Payment Web  2013/05/02

PW Interview: Stephen Balaban of Lamda Labs on the Face Recognition API  2013/04/29

PW Interview: Amber Feng, Stripe API, The Payment Web 2013/04/24

PW Interview: Greg Lamp and Austin Ogilvie of Yhat on Shipping Predictive Models via API   2013/04/22

Google Mirror API documentation is open for developers   2013/04/18

PW Interview: Ricky Robinett, Ordr.in API, Ordering Food meets API    2013/04/16

PW Interview: Jacob Perkins, Text Processing API, NLP meets API   2013/04/10

Amazon EC2 On Demand Windows Instances -Prices reduced by 20%  2013/04/08

Amazon S3 API Requests prices slashed by half  2013/04/02

PW Interview: Stuart Battersby, Chatterbox API, Machine Learning meets Social 2013/04/02

PW Interview: Karthik Ram, rOpenSci, Wrapping all science API2013/03/20

Viralheat Human Intent API- To buy or not to buy 2013/03/13

Interview Tammer Kamel CEO and Founder Quandl 2013/03/07

YHatHQ API: Calling Hosted Statistical Models 2013/03/04

Quandl API: A Wikipedia for Numerical Data 2013/02/25

Amazon Redshift API is out of limited preview and available! 2013/02/18

Windows Azure Media Services REST API 2013/02/14

Data Science Toolkit Wraps Many Data Services in One API 2013/02/11

Diving into Codeacademy’s API Lessons 2013/01/31

Google APIs finetuning Cloud Storage JSON API 2013/01/29

2012
Ergast API Puts Car Racing Fans in the Driver’s Seat 2012/12/05
Springer APIs- Fostering Innovation via API Contests 2012/11/20
Statistically programming the web – Shiny,HttR and RevoDeploy API 2012/11/19
Google Cloud SQL API- Bigger ,Faster and now Free 2012/11/12
A Look at the Web’s Most Popular API -Google Maps API 2012/10/09
Cloud Storage APIs for the next generation Enterprise 2012/09/26
Last.fm API: Sultan of Musical APIs 2012/09/12
Socrata Data API: Keeping Government Open 2012/08/29
BigML API Gets Bigger 2012/08/22
Bing APIs: the Empire Strikes Back 2012/08/15
Google Cloud SQL: Relational Database on the Cloud 2012/08/13
Google BigQuery API Makes Big Data Analytics Easy 2012/08/05
Your Store in The Cloud -Google Cloud Storage API 2012/08/01
Predict the future with Google Prediction API 2012/07/30
The Romney vs Obama API 2012/07/27

BigML creates a marketplace for Predictive Models

BigML has created a marketplace for selling Datasets and Models. This is a first (?) as the closest market for Predictive Analytics till now was Rapid Miner’s marketplace for extensions (at http://rapidupdate.de:8180/UpdateServer/faces/index.xhtml)

From http://blog.bigml.com/2012/10/25/worlds-first-predictive-marketplace/

SELL YOUR DATA

You can make your Dataset public. Mind you: the Datasets we are talking about are BigML’s fancy histograms. This means that other BigML users can look at your Dataset details and create new models based on this Dataset. But they can not see individual records or columns or use it beyond the statistical summaries of the Dataset. Your Source will remain private, so there is no possibility of anyone accessing the raw data.

SELL YOUR MODEL

Now, once you have created a great model, you can share it with the rest of the world. For free or at any price you set.Predictions are paid for in BigML Prediction Credits. The minimum price is ‘Free’ and the maximum price indicated is 100 credits.

White Box Models

Clicking on the white open lock will open up your model to the rest of the world. Anyone can now buy your model, explore it, use it to make predictions

Black Box Models

If you choose the black box setting (the black open lock icon), other BigML users will NOT be able to view or clone your model, but they will be able to use it to make predictions.

——

DOWNLOAD YOUR MODEL

BigML.com have added downloads to our models. Simply choose the format you want and you can copy/paste the code or text. There is a range of formats that they offer currently: JSON PML, PMML, Python, Ruby, Objective-C, Java, the rules of the decision tree in plain text and a Summary overview of your model. Around the corner are MS Excel downloads and R (of course!).

PUBLICIZE YOUR MODEL

There’s also an ‘embed’ function, so now you can embed the little poster of your model in your blog post or website, so it is easy to share it in your own environment.

————————————————————————————————————————–

It is nice to see Models and Data getting the APPY treatment and hopefully, it will encourage other vendors Iike Google Prediction API etc to further spend thought and effort to reward data mining individuals directly without going through corporate intermediaries while ensuring intellectual property safeguards .

An R package market for enterprises? for Python libraries? JMP addins? A market for SAS Macros- who knows what the future shall hold. But overall, this is a very positive step by the BigML.com team. The App marketplace has helped revolutionize mobile and desktop computing and hopefully it will do the same for Business Analytics.

 

Interview BigML.com

Here is an interview with Charlie Parker, head of large scale online algorithms at http://bigml.com

Ajay-  Describe your own personal background in scientific computing, and how you came to be involved with machine learning, cloud computing and BigML.com

Charlie- I am a machine learning Ph.D. from Oregon State University. Francisco Martin (our founder and CEO), Adam Ashenfelter (the lead developer on the tree algorithm), and myself were all studying machine learning at OSU around the same time. We all went our separate ways after that.

Francisco started Strands and turned it into a 100+ million dollar company building recommender systems. Adam worked for CleverSet, a probabilistic modeling company that was eventually sold to Cisco, I believe. I worked for several years in the research labs at Eastman Kodak on data mining, text analysis, and computer vision.

When Francisco left Strands to start BigML, he brought in Justin Donaldson who is a brilliant visualization guy from Indiana, and an ex-Googler named Jose Ortega who is responsible for most of our data infrastructure. They pulled in Adam and I a few months later. We also have Poul Petersen, a former Strands employee, who manages our herd of servers. He is a wizard and makes everyone else’s life much easier.

Ajay- You use clojure for the back end of BigML.com .Are there any other languages and packages you are considering? What makes clojure such a good fit for cloud computing ?

Charlie- Clojure is a great language because it offers you all of the benefits of Java (extensive libraries, cross-platform compatibility, easy integration with things like Hadoop, etc.) but has the syntactical elegance of a functional language. This makes our code base small and easy to read as well as powerful.

We’ve had occasional issues with speed, but that just means writing the occasional function or library in Java. As we build towards processing data at the Terabyte level, we’re hoping to create a framework that is language-agnostic to some extent. So if we have some great machine learning code in C, for example, we’ll use Clojure to tie everything together, but the code that does the heavy lifting will still be in C. For the API and Web layers, we use Python and Django, and Justin is a huge fan of HaXe for our visualizations.

 Ajay- Current support is for Decision Trees. When can we see SVM, K Means Clustering and Logit Regression?

Charlie- Right now we’re focused on perfecting our infrastructure and giving you new ways to put data in the system, but expect to see more algorithms appearing in the next few months. We want to make sure they are as beautiful and easy to use as the trees are. Without giving too much away, the first new thing we will probably introduce is an ensemble method of some sort (such as Boosting or Bagging). Clustering is a little further away but we’ll get there soon!

Ajay- How can we use the BigML.com API using R and Python.

Charlie- We have a public github repo for the language bindings. https://github.com/bigmlcom/io Right now, there there are only bash scripts but that should change very soon. The python bindings should be there in a matter of days, and the R bindings in probably a week or two. Clojure and Java bindings should follow shortly after that. We’ll have a blog post about it each time we release a new language binding. http://blog.bigml.com/

Ajay-  How can we predict large numbers of observations using a Model  that has been built and pruned (model scoring)?

Charlie- We are in the process of refactoring our backend right now for better support for batch prediction and model evaluation. This is something that is probably only a few weeks away. Keep your eye on our blog for updates!

Ajay-  How can we export models built in BigML.com for scoring data locally.

Charlie- This is as simple as a call to our API. https://bigml.com/developers/models The call gives you a JSON object representing the tree that is roughly equivalent to a PMML-style representation.

About-

You can read about Charlie Parker at http://www.linkedin.com/pub/charles-parker/11/85b/4b5 and the rest of the BigML team at

https://bigml.com/team