Best practices for serverless inference

Serverless inference is a cloud computing model that allows for deploying and serving machine learning models without managing underlying infrastructure. It offers several advantages, particularly for expensive transformer-based models, including cost-efficiency, scalability, reduced operational overhead, and flexibility. Serverless inference eliminates idle GPU time costs, automatically scales to handle varying loads, and reduces the need for manual server management. While it may appear more expensive on a per-minute basis compared to traditional deployments, it can lead to significant cost savings for workloads with variable demand. Various cloud providers offer serverless capabilities, including Google Cloud Run Functions, which supports running GPUs, but is currently in preview. To optimize serverless inference deployments, it's essential to leverage GPU acceleration, minimize cold starts, optimize model loading and initialization, implement efficient batching, and follow best practices for each of these aspects.

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