Machine Learning (ML)-based System

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A Machine Learning (ML)-based System is a automated data-driven system that include learning system which implement machine learning algorithms (to solve a machine learning-based task).






  • Martin Zinkevich. (2017). “Rules of Machine Learning: Best Practices for ML Engineering."
    • QUOTE: … Most of the problems you will face are, in fact, engineering problems. … So, the basic approach is: … make sure your pipeline is solid end to end
      • Rule #1: Don’t be afraid to launch a product without machine learning.
      • Rule #2: Make metrics design and implementation a priority.
      • Rule #3: Choose machine learning over a complex heuristic.
      • Rule #4: Keep the first model simple and get the infrastructure right.
      • Rule #5: Test the infrastructure independently from the machine learning.
      • Rule #6: Be careful about dropped data when copying pipelines.
      • Rule #7: Turn heuristics into features, or handle them externally.


  • (IEEE, 2013) ⇒
    • The robot hardware is progressively becoming more complex, which leads to growing interest in applying machine learning and statistics approaches within the robotics community. At the same time, there has been a growth within the machine learning community in using robots as motivating applications for new algorithms and formalisms. Considerable evidence of this exists in the use of robot learning approaches in high-profile competitions such as RoboCup and the DARPA Challenges, and the growing number of research programs funded by governments around the world. Additionally, the volume of research is increasing, as shown by the number of robot learning papers accepted to IROS and ICRA, and the corresponding number of learning sessions.