From the course: Mistakes to Avoid in Machine Learning

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Developing in a silo

Developing in a silo

From the course: Mistakes to Avoid in Machine Learning

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Developing in a silo

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- Machine learning is tough work. It requires a lot of dedicated concentration to build a successful machine learning pipeline. With so much time spent heads down in your code, it's easy to lose perspective about some of the intangibles that go into a successful machine learning project. So avoid the mistake of developing in a silo through these tips. First, take the vulnerable step to invite others into your code. When you first start out in data science, it can be nerve wracking to share your work. You're worried you'll be judged for a simplistic approach you've used, or that someone will point out errors in your script. And that's okay. More experienced team members will likely have great recommendations for you. And those with less experience, will have questions that will prepare you to socialize your work to a broader audience. Perhaps you're approaching something in an entirely new way for your organization. I…

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