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