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Amazon SageMaker launches new inference capabilities to reduce costs and latency

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We are excited to announce new capabilities on Amazon SageMaker which help customers reduce model deployment costs by 50% on average and achieve 20% lower inference latency on average. Customers can deploy multiple models to the same instance to better utilize the underlying accelerators. SageMaker actively monitors instances that are processing inference requests and intelligently routes requests based on which instances are available.

Announcing API support for creating Amazon SageMaker Notebook jobs

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Amazon SageMaker notebook jobs allows data scientists to run their notebooks on demand or on a schedule with a few clicks on Amazon SageMaker Studio, a web-based IDE for machine learning (ML). Today, we’re excited to announce that you can programmatically run notebooks as jobs using APIs provided by SageMaker Pipelines, SageMaker’s ML workflow orchestration service. Furthermore, you can create a multi-step ML workflow with multiple dependent notebooks using these APIs.

SageMaker now provides improved SDK tooling and UX for model deployment

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We are excited to announce new tools and improvements that enable customers to reduce the time from days to hours to deploy machine learning (ML) models including foundation models (FMs) on Amazon SageMaker for Inference at scale . This includes a new Python SDK library that simplifies the process of packaging and deploying a ML model on SageMaker from seven steps to one with an option to do local inference. Further, Amazon SageMaker is offering new interactive UI experiences in Amazon SageMaker Studio that will help customers quickly deploy their trained ML model or FMs using performant and cost-optimized configurations in as few as three clicks.

Amazon SageMaker Canvas now supports natural language instructions for data preparation

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Amazon SageMaker Canvas now supports natural language instructions for data exploration, visualization, and preparation to build machine learning (ML) models. Amazon SageMaker Canvas is a no-code tool that enables customers to easily create highly accurate ML models without writing a line of code. Starting today, you can use FM-powered natural language instructions enabled by Amazon Bedrock for data preparation. This new capability enables you to interact with your data, ask questions, visualize feature distribution and correlations, and transform data to the right structure for your business problems, using natural language queries.

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