Difference between revisions of "Development"
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| − | + | # More emphasis on information pipeline management; data collection, preparation, feature determination, and pipeline configuration management. | |
| + | # Developing a machine learning application is more iterative and explorative process than traditional software engineering. Learning / Testing / Validation of models is an upfront task | ||
Revision as of 19:03, 17 June 2019
Youtube search... ...Google search
- Case Studies
- 6 Ways AI Transforms Software Development | Mariya Yao - MetaMaven
- Machine Learning vs Traditional Programming | Oleksii Kharkovyna - Towards Data Science - Medium
- Web-Based Monte Carlo Simulation for Agile Estimation
- Why AI & ML Will Shake Software Testing up in 2019 | Oleksii Kharkovyna - Medium
- Track: Solving Software Engineering Problems with Machine Learning | Cyril Magnin III - QCon.ai
- Automated Machine Learning (AML) - AutoML
Major differences:
- More emphasis on information pipeline management; data collection, preparation, feature determination, and pipeline configuration management.
- Developing a machine learning application is more iterative and explorative process than traditional software engineering. Learning / Testing / Validation of models is an upfront task
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
