Thursday, April 23, 2009
Apache Mahout Scalable Machine Learning first public release
The Mahoot project has just made their first public release of scalable machine learning tools for the Hadoop platform. With Amazon's Elastic MapReduce, it is possible (for example) to make an 8 server instance 1 hour run for about a dollar - combined with Mahoot, I think that this is really going to open the door for individuals and small organizations to more effectively use machine learning. Good stuff! I have started to take a quick look at the code but I won't have time to try it out on Elastic MapReduce for a few weeks (I am finishing the last Chapter of my Intelligent Scripting for Web 3.0 book and then I have some production work to do - so no free time for a while!)
It is interesting in life how things often come together just when you need them. I have a business idea that I want to pursue using EC2 and Mahout will probably help with a small part of the system.
It is interesting in life how things often come together just when you need them. I have a business idea that I want to pursue using EC2 and Mahout will probably help with a small part of the system.
Labels: AI, Java, machine learning
Wednesday, August 29, 2007
Good book: "Programming Collective Intelligence"
This book
is a great introduction to the techniques that I use almost daily in my own personal research and work for customers, and I can recommend it without reservation. The choice of Python for the examples is not optimum for me, but OK, especially because the techniques in the book for machine learning, categorization, clustering, filtering, optimization, support vector machines, etc. are mostly short and can be used as is or converted to whatever programming language that you need to use. The data used to present the book material is mostly from collaborative web sites. The book relies heavily on existing Python libraries and I like this approach since it mirrors rational software development practice: build custom code on top of existing libraries and software tools. Good book!
is a great introduction to the techniques that I use almost daily in my own personal research and work for customers, and I can recommend it without reservation. The choice of Python for the examples is not optimum for me, but OK, especially because the techniques in the book for machine learning, categorization, clustering, filtering, optimization, support vector machines, etc. are mostly short and can be used as is or converted to whatever programming language that you need to use. The data used to present the book material is mostly from collaborative web sites. The book relies heavily on existing Python libraries and I like this approach since it mirrors rational software development practice: build custom code on top of existing libraries and software tools. Good book!Labels: AI, clustering, machine learning, Python, support vector machines
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