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

AWS Machine Learning Blog

The Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning (ML) models on Amazon SageMaker. Starting with SageMaker Python SDK version 2.148.0, Provide a name for the stack (for example, networking-stack ), and complete the remaining steps to create the stack.

Python 109
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Optimize deployment cost of Amazon SageMaker JumpStart foundation models with Amazon SageMaker asynchronous endpoints

AWS Machine Learning Blog

These models have revolutionized various computer vision (CV) and natural language processing (NLP) tasks, including image generation, translation, and question answering. Python 3.10 The notebook queries the endpoint in three ways: the SageMaker Python SDK, the AWS SDK for Python (Boto3), and LangChain.

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Top Artificial Intelligence (AI) Tools That Can Generate Code To Help Programmers

Marktechpost

Tabnine Although Tabnine is not an end-to-end code generator, it amps up the integrated development environment’s (IDE) auto-completion capability. Jacob Jackson created Tabnine in Rust when he was a student at the University of Waterloo, and it has now grown into a complete AI-based code completion tool.

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Announcing Rekogniton Custom Moderation: Enhance accuracy of pre-trained Rekognition moderation models with your data

AWS Machine Learning Blog

Content moderation in Amazon Rekognition Amazon Rekognition is a managed artificial intelligence (AI) service that offers pre-trained and customizable computer vision capabilities to extract information and insights from images and videos. Upload images from your computer and provide labels. Choose Create project.

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Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation

Flipboard

We also discuss how to transition from experimenting in the notebook to deploying your models to SageMaker endpoints for real-time inference when you complete your prototyping. After confirming your quota limit, you need to complete the dependencies to use Llama 2 7b chat. Python 3.10 transformers==4.33.0 accelerate==0.21.0

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Improve performance of Falcon models with Amazon SageMaker

AWS Machine Learning Blog

The decode phase includes the following: Completion – After the prefill phase, you have a partially generated text that may be incomplete or cut off at some point. The decode phase is responsible for completing the text to make it coherent and grammatically correct. The default is 32.

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Improve throughput performance of Llama 2 models using Amazon SageMaker

AWS Machine Learning Blog

Large language models (LLMs) used to generate text sequences need immense amounts of computing power and have difficulty accessing the available high bandwidth memory (HBM) and compute capacity. The inference server handles this automatically, so no client-side code changes are needed.