machine learning deployment

An overview of real-world AI hosting case studies, comparing cloud, hybrid, and edge deployments for training and inference. It examines architecture choices, GPU provisioning, autoscaling, latency, and cost optimization, plus MLOps, security, and compliance. Practical lessons and metrics reveal common pitfalls and proven patterns to improve reliability, performance, and total cost of ownership across varied workloads.

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