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Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning

Customers are now pre-training and fine-tuning LLMs ranging from 1 billion to over 175 billion parameters to optimize model performance for applications across industries, from healthcare to finance and marketing. For more information on how to enable SMP with your existing PyTorch FSDP training scripts, refer to Get started with SMP.

Scripts 130
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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time. On the other hand, generative artificial intelligence (AI) models can learn these templates and produce coherent scripts when fed with quarterly financial data.

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CBRE and AWS perform natural language queries of structured data using Amazon Bedrock

AWS Machine Learning

Services range from financing and investment to property management. AWS Prototyping successfully delivered a scalable prototype, which solved CBRE’s business problem with a high accuracy rate (over 95%) and supported reuse of embeddings for similar NLQs, and an API gateway for integration into CBRE’s dashboards.

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Configure and use defaults for Amazon SageMaker resources with the SageMaker Python SDK

AWS Machine Learning

Enterprise customers in tightly controlled industries such as healthcare and finance set up security guardrails to ensure their data is encrypted and traffic doesn’t traverse the internet. Additionally, each API call can have its own configurations. Then it copies the file into the default location for Studio notebooks.

APIs 114
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Simplify access to internal information using Retrieval Augmented Generation and LangChain Agents

AWS Machine Learning

Amazon API Gateway hosts a REST API with various endpoints to handle user requests that are authenticated using Amazon Cognito. Finally, the response is sent back to the user via a HTTPs request through the Amazon API Gateway REST API integration response. The web application front-end is hosted on AWS Amplify.

APIs 130
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Inpaint images with Stable Diffusion using Amazon SageMaker JumpStart

AWS Machine Learning

In this post, we present a comprehensive guide on deploying and running inference using the Stable Diffusion inpainting model in two methods: through JumpStart’s user interface (UI) in Amazon SageMaker Studio , and programmatically through JumpStart APIs available in the SageMaker Python SDK.

Scripts 97
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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. Having a unified developer experience when accessing custom models or base models through Amazon Bedrock’s API. Ease of deployment through a fully managed, serverless, service. 2, 3, 3.1,

APIs 143