In this presentation, find out how Dr. Greg Benson brought ML into the SnapLogic platform and how to combine the strengths of industry practices and academic methodologies to achieve success with ML.
4. 1992 early academic work
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Learning continuous-space navigation heuristics in real-time
Gregory D. Benson and Armand Prieditis
Appears In:
From Animals to Animats: Second
International Conference on Simulation of
Adaptive Behavior
- Honolulu, Hawaii, December 1992
Figure 1: An Example Navigation Problem
5. 2010 first industry ML project - predictive field linking
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• First work with real data
• Lots of data analysis
• Model building
• Experimentation
• Production
8. Academic/industry collaboration in practice
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• Professors like to work with students
• Professors like to work on interesting problems
• Professors *need* to publish
• Hire university faculty part-time so they continue teaching
• How it works: USF and SnapLogic
9. What is machine learning?
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• Machine learning is software
• Machine learning is driven by data
• Machine learning is continuous
• Machine learning can be fickle and flawed
10. Challenges with ML/AI projects
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• Finding talent
• Access to data
• Hard to quantify the benefits and impact on revenue
• You need more than just a data scientist
11. Getting ML/AI talent
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• Work with professors and students
• Fund online ML/AI classes for current engineers
• Consider having your data scientists and engineers
teach at local Universities
• Make your data available for research
12. How to think about ML/AI projects
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• Enable access to data (and not just once)
• Understand the experimental nature of ML
development
• Allocate deployment resources up front
• Develop a plan to determine the success of an ML
deployment
• Consider self-service data science
13. Figure 1: An Example Navigation Problem
Define your goal
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