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Pitfalls (Avoid)
User Trust and Engagement
Explainability
Avoid notebooks in production
Poor security practices
Don’t treat accuracy as the only or even the best way to evaluate your algorithm
Use machine learning judiciously
Don’t forget to understand the at-inference usage profile
Don’t make it difficult for a data scientists to access data or use the tools they need
Not taking into consideration the downstream application of the model
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Pitfalls (Avoid)

Here are the articles in this section:
User Trust and Engagement
Explainability
Avoid notebooks in production
Poor security practices
Don’t treat accuracy as the only or even the best way to evaluate your algorithm
Use machine learning judiciously
Don’t forget to understand the at-inference usage profile
Don’t make it difficult for a data scientists to access data or use the tools they need
Not taking into consideration the downstream application of the model
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User Trust and Engagement
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