Machine Learning Hindsight
January 18, 2016
Have you ever found yourself saying, “If I only knew then, what I know now”? It is a moment we all experience, but instead of stewing over our past mistakes it is better to share the lessons we’ve learned with others. Data scientist Peadar Coyle learned some valuable lessons when he first started working with machine learning. He discusses three main things he learned in the article, “Three Things I Wish I Knew Earlier About Machine Learning.”
Here are the three items he wishes he knew then about machine learning, but know now:
- “Getting models into production is a lot more than just micro services
- Feature selection and feature extraction are really hard to learn from a book
- The evaluation phase is really important”
Developing models is an easy step, but putting them in production is difficult. There are many major steps that need attending to and doing all of the little jobs isn’t feasible on huge projects. Peadar recommends outsourcing when you can. Books and online information are good reference tools, but when they cannot be applied to actual situations the knowledge is useless. Paedar learned that real world experience has no comparison. When it comes to testing, it is a very important thing. Very much as real world experience is invaluable, so is the evaluation. Life does not hand perfect datasets for experimentation and testing different situations will better evaluate the model.
Paedar’s advice applies to machine learning, but it applies more to life in general.
Whitney Grace, January 18, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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