Difference between revisions of "Development"

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* [http://qcon.ai/qconai2019/track/solving-software-engineering-problems-machine-learning Track: Solving Software Engineering Problems with Machine Learning | Cyril Magnin III - QCon.ai]
 
* [http://qcon.ai/qconai2019/track/solving-software-engineering-problems-machine-learning Track: Solving Software Engineering Problems with Machine Learning | Cyril Magnin III - QCon.ai]
 
* [[Automated Machine Learning (AML) - AutoML]]
 
* [[Automated Machine Learning (AML) - AutoML]]
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Major differences:
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1. More emphasis on information pipeline management; data collection, preparation, feature determination, and pipeline configuration management. 
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2. Developing a machine learning application is more iterative and explorative process than traditional software engineering.  Learning / Testing / Validation of models is an upfront task
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http://images.contentful.com/pqts2v0qq7kz/4Mcjw0xAi4auqweOQQyWCu/80236c975b5026ec67e61f767a646b45/machine_learning_flow--4j88rajonr_s600x0_q80_noupscale.png
 
http://images.contentful.com/pqts2v0qq7kz/4Mcjw0xAi4auqweOQQyWCu/80236c975b5026ec67e61f767a646b45/machine_learning_flow--4j88rajonr_s600x0_q80_noupscale.png

Revision as of 19:02, 17 June 2019

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Major differences: 1. More emphasis on information pipeline management; data collection, preparation, feature determination, and pipeline configuration management. 2. Developing a machine learning application is more iterative and explorative process than traditional software engineering. Learning / Testing / Validation of models is an upfront task


machine_learning_flow--4j88rajonr_s600x0_q80_noupscale.png

Developing a machine learning application is even more iterative and explorative process than software engineering. Machine learning is applied on problems that are too complicated for humans to figure out (that is why we ask a computer to find a solution for us!). Differences between machine learning and software engineering | Antti Ajanki - Futurice