# Pitfalls (Avoid)

- [User Trust and Engagement](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/user-trust-and-engagement.md)
- [Explainability](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/explainability.md)
- [Avoid  notebooks in production](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/avoid-notebooks-in-production.md)
- [Poor security practices](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/poor-security-practices.md)
- [Don’t treat accuracy as the only or even the best way to  evaluate your algorithm](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/dont-treat-accuracy-as-the-only-or-even-the-best-way-to-evaluate-your-algorithm.md)
- [Use machine learning judiciously](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/use-machine-learning-judiciously.md)
- [Don’t forget to understand the at-inference usage profile](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/dont-forget-to-understand-the-at-inference-usage-profile.md)
- [Don’t make it difficult for a data scientists to access data or use the tools they need](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/dont-make-it-difficult-for-a-data-scientists-to-access-data-or-use-the-tools-they-need.md)
- [Not taking into consideration the downstream application of the model](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/not-taking-into-consideration-the-downstream-application-of-the-model.md)


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