Amazon SageMaker HyperPod now supports model caching for faster inference autoscaling and reduced cold starts

Amazon SageMaker HyperPod now supports model caching, an inference optimization that pre-loads model weights and container images onto cluster nodes so pods start ...

#AWS #AmazonSagemaker

🆕 Amazon SageMaker Feature Store now lets you update individual features, cutting write latency. Data scientists can modify specific features without rewriting records, reducing costs and latency. Available globally. For details, see Amazon Feature Store Runtime, Standard V2…

#AWS #AmazonSagemaker

Amazon SageMaker Feature Store now supports individual feature updates to lower write latency

Amazon SageMaker Feature Store is a fully managed capability that makes it easy to compute, store, and retrieve features for training and deploying AI models. SageMaker Feature St...

#AWS #AmazonSagemaker

Businesses in healthcare, retail, and life sciences can now harness the power of predictive data insights with Amazon SageMaker Canvas, a no-code ML workflow that connects seamlessly to Snowflake. #AmazonSageMaker #Snowflake #DataInsights #ML 📊🔍