标记有以下标签的文章： 亚马逊 SageMaker 部署

Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your needs, whether real time or batch. Once you deploy a model, SageMaker creates persistent endpoints to integrate into your applications to make ML predictions (also known as inference). It supports the entire spectrum of inference, from low latency (a few milliseconds) and high throughput (hundreds of thousands of inference requests per second) to long-running inference for use cases such as natural language processing (NLP) and computer vision (CV). Whether you bring your own models and containers or use those provided by AWS, you can implement MLOps best practices using SageMaker to reduce the operational burden of managing ML models at scale.

内容语言: 中文 (简体)

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