What are the key security considerations for ML deployment?
An example of an AI chatbot conversation about security in machine learning deployment.
What are the key security considerations for ML deployment?
Key considerations include data privacy, model integrity, access control, and monitoring for adversarial attacks.
How can I ensure data privacy during deployment?
Implement encryption, anonymization, and strict access controls to protect sensitive data.
What are adversarial attacks in ML?
Adversarial attacks involve manipulating input data to deceive the model, compromising its performance.
How can I protect my model from these attacks?
You can use techniques like adversarial training, input validation, and robust model architectures to enhance security.
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