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Please join us for our next meet-up with Max Lin, technical lead of the Google Prediction API team! Join us at 6:30 for networking, the presentation will start promptly at 7pm.
Description:
Finding patterns in big data is no longer a question of when, but rather how soon. Even with the aid of machine learning tools, analyzing big data is challenging: resources need to be provisioned, algorithms need to be scaled, and predictive models need to be quickly integrated with other software.
The Google Prediction API now adds Google’s machine learning solutions to developers’ toolkits through a simple, three-step process. In this talk we will introduce the advantages of machine learning on the cloud, and discuss how the Google Prediction API alleviates the restrictions of resource provisioning, scalability, and integration in big data analysis.
Max Lin is a software engineer with Google Research in the New York City office, and the tech lead of the Google Prediction API team. Prior to Google, he published research work in video content analysis, sentiment analysis, machine learning, and cross-lingual information retrieval. He graduated with a PhD degree in Computer Science from Carnegie Mellon University.
For more about the Google Prediction API:
Homepage: http://code.google.co...
Getting Started Guide: http://code.google.co...
We look forward to seeing you there!
Bruno & Alex
August 11, 2010
I had to miss the last part of the talk (just when it got to the good geeky stuff), but overall the meetup was great.
August 7, 2010
I am registering for the API, as I have a number of cluster identification and affinity type applications for which the Google Prediction API might be ideal for. I would love to help push refinement of the tools capabilities.
August 6, 2010
Good information presented. Well navigated questions by Max and Travis. Super hosting by Razorfish. Will the presentations (RazorFish & Google's) be made available?
August 6, 2010
Good hosting by Razorfish! The Google Prediction API is an interesting technology that will be useful for a particular set of applications. Specifically, I suspect it will be useful for problems with very large amounts of data and a reasonably small solution space, where a "quick and dirty" answer is most important. It's unclear the value on smaller problems or where the feature space is large or ill-defined. I suspect the "black box" nature of the system will lead to limitations in practical use that have not yet been recognized. As far as the presentation goes, I do wish that the presenter had been more prepared to answer questions in the way that an academic researcher presenting a new ML technology would have answered questions. Certainly much of the audience expected as much.
August 6, 2010
Learned things, and it was good.
August 6, 2010
Max did a great job. I really enjoyed the discussion on the large scale distributed supervised learning.
August 5, 2010
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It was informative.