Best practices for building secure applications with Amazon Transcribe

Favorite Amazon Transcribe is an AWS service that allows customers to convert speech to text in either batch or streaming mode. It uses machine learning–powered automatic speech recognition (ASR), automatic language identification, and post-processing technologies. Amazon Transcribe can be used for transcription of customer care calls, multiparty conference calls, and

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Shared by AWS Machine Learning March 26, 2024

Enhance performance of generative language models with self-consistency prompting on Amazon Bedrock

Favorite Generative language models have proven remarkably skillful at solving logical and analytical natural language processing (NLP) tasks. Furthermore, the use of prompt engineering can notably enhance their performance. For example, chain-of-thought (CoT) is known to improve a model’s capacity for complex multi-step problems. To additionally boost accuracy on tasks

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Shared by AWS Machine Learning March 19, 2024

Unlock the potential of generative AI in industrial operations

Favorite In the evolving landscape of manufacturing, the transformative power of AI and machine learning (ML) is evident, driving a digital revolution that streamlines operations and boosts productivity. However, this progress introduces unique challenges for enterprises navigating data-driven solutions. Industrial facilities grapple with vast volumes of unstructured data, sourced from

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Shared by AWS Machine Learning March 19, 2024

Fine-tune Code Llama on Amazon SageMaker JumpStart

Favorite Today, we are excited to announce the capability to fine-tune Code Llama models by Meta using Amazon SageMaker JumpStart. The Code Llama family of large language models (LLMs) is a collection of pre-trained and fine-tuned code generation models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned

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Shared by AWS Machine Learning March 18, 2024

Optimize price-performance of LLM inference on NVIDIA GPUs using the Amazon SageMaker integration with NVIDIA NIM Microservices

Favorite NVIDIA NIM microservices now integrate with Amazon SageMaker, allowing you to deploy industry-leading large language models (LLMs) and optimize model performance and cost. You can deploy state-of-the-art LLMs in minutes instead of days using technologies such as NVIDIA TensorRT, NVIDIA TensorRT-LLM, and NVIDIA Triton Inference Server on NVIDIA accelerated

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Shared by AWS Machine Learning March 18, 2024

Transform one-on-one customer interactions: Build speech-capable order processing agents with AWS and generative AI

Favorite In today’s landscape of one-on-one customer interactions for placing orders, the prevailing practice continues to rely on human attendants, even in settings like drive-thru coffee shops and fast-food establishments. This traditional approach poses several challenges: it heavily depends on manual processes, struggles to efficiently scale with increasing customer demands,

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Shared by AWS Machine Learning March 15, 2024

Best practices to build generative AI applications on AWS

Favorite Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However, adoption of these FMs involves addressing some key challenges, including quality output, data privacy, security, integration with organization data, cost, and skills to deliver. In

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Shared by AWS Machine Learning March 14, 2024