This article discusses key questions related to deployment and serving of machine learning models using AWS SageMaker. It explores topics such as scaling deployed models, rolling out new versions, collaborating among data scientists, packaging ML models, tracking predicted decisions, and ensuring scalability. The article also provides practical steps and guidelines for creating an endpoint, choosing the appropriate endpoint type, packaging ML models with SageMaker Neo, promoting models with endpoint configurations, and automating processes for A/B testing, canary deployments, and traffic migration. Additionally, it covers mechanisms for rollback and automatic scaling of deployed models.
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