Favorite In Part 1 of this series, we explored best practices for creating accurate and reliable agents using Amazon Bedrock Agents. Amazon Bedrock Agents help you accelerate generative AI application development by orchestrating multistep tasks. Agents use the reasoning capability of foundation models (FMs) to create a plan that decomposes
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Shared by AWS Machine Learning October 26, 2024
Favorite Many organizations are building generative AI applications powered by large language models (LLMs) to boost productivity and build differentiated experiences. These LLMs are large and complex and deploying them requires powerful computing resources and results in high inference costs. For businesses and researchers with limited resources, the high inference
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Shared by AWS Machine Learning October 26, 2024
Favorite Generative AI adoption among various industries is revolutionizing different types of applications, including image editing. Image editing is used in various sectors, such as graphic designing, marketing, and social media. Users rely on specialized tools for editing images. Building a custom solution for this task can be complex. However,
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Shared by AWS Machine Learning October 26, 2024
Favorite Today, we’re pleased to announce the general availability (GA) of Amazon Bedrock Custom Model Import. This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. Whether leveraging fine-tuned models like Meta Llama, Mistral Mixtral, and IBM Granite, or
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Shared by AWS Machine Learning October 26, 2024
Favorite Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. Extensively used by data scientists and ML engineers across various industries, this robust tool provides high availability and uninterrupted access for
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Shared by AWS Machine Learning October 26, 2024
Favorite You can now create an end-to-end workflow to train, fine tune, evaluate, register, and deploy generative AI models with the visual designer for Amazon SageMaker Pipelines. SageMaker Pipelines is a serverless workflow orchestration service purpose-built for foundation model operations (FMOps). It accelerates your generative AI journey from prototype to
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Shared by AWS Machine Learning October 26, 2024
Favorite To stay competitive, businesses across industries use foundation models (FMs) to transform their applications. Although FMs offer impressive out-of-the-box capabilities, achieving a true competitive edge often requires deep model customization through pre-training or fine-tuning. However, these approaches demand advanced AI expertise, high performance compute, fast storage access and can
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Shared by AWS Machine Learning October 26, 2024
Favorite This post is co-written with Harrison Chase, Erick Friis and Linda Ye from LangChain. Generative AI is set to revolutionize user experiences over the next few years. A crucial step in that journey involves bringing in AI assistants that intelligently use tools to help customers navigate the digital landscape.
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Shared by AWS Machine Learning October 26, 2024
Favorite In today’s customer-centric business world, providing exceptional customer service is crucial for success. Contact centers play a vital role in shaping customer experiences, and analyzing post-call interactions can provide valuable insights to improve agent performance, identify areas for improvement, and enhance overall customer satisfaction. Amazon Web Services (AWS) has
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Shared by AWS Machine Learning October 26, 2024
Favorite Have you ever faced the challenge of obtaining high-quality data for fine-tuning your machine learning (ML) models? Generating synthetic data can provide a robust solution, especially when real-world data is scarce or sensitive. For instance, when developing a medical search engine, obtaining a large dataset of real user queries
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Shared by AWS Machine Learning October 26, 2024
Shared by AWS Machine Learning October 26, 2024
