Lessons Learned from Building Machine Learning Systems
Last Updated on September 5, 2016
In a recent presentation at MLConf, Xavier Amatriain described 10 lessons that he has learned about building machine learning systems as the Research/Engineering Manager at Netflix.
In this you will discover these 10 lessons in a summary from his talk and slides.
Lessons Learned from Building Machine Learning Systems Taken from Xavier’s presentation
10 Lessons Learned
The 10 lessons that Xavier presents can be summarized as follows:
- More data vs./and Better Models
- You might not need all your Big Data
- The fact that a more complex model does not improve things does not mean you don’t need one
- Be thoughtful about your training data
- Learn to deal with (The curse of) Presentation Bias
- The UI is the algorithm’s only communication channel with that which matters most: the users
- Data and Models are great. You know what’s even better? The right evaluation approach
- Distributing algorithms is important, but knowing at what level to do it is even more important
- It pays
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