Favorite Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. Examples include financial systems processing transaction data streams, recommendation engines processing user activity data, and computer vision models processing video frames. In these scenarios, customized model monitoring for
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Shared by AWS Machine Learning October 28, 2024
Favorite In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This post presents an architectural approach to extract data from different cloud environments, such as
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Shared by AWS Machine Learning October 28, 2024
Favorite RALEIGH, N.C., Oct. 28, 2024 — ALL THINGS OPEN 2024 — After a year-long, global, community design process, the Open Source Definition (OSAID) v.1.0 is available for public use. The release of version 1.0 was announced today at All Things Open 2024, an industry conference focused on common issues of interest to the
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Shared by voicesofopensource October 28, 2024
Favorite As Sub-Saharan Africa looks to its ‘digital decade’, President of Google EMEA Matt Brittin discusses the big opportunities and some new announcements from Google. View Original Source (blog.google/technology/ai/) Here.
Favorite With the advent of generative AI and machine learning, new opportunities for enhancement became available for different industries and processes. During re:Invent 2023, we launched AWS HealthScribe, a HIPAA eligible service that empowers healthcare software vendors to build their clinical applications to use speech recognition and generative AI to
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Shared by AWS Machine Learning October 26, 2024
Favorite This post is co-written by Rodrigo Amaral, Ashwin Murthy and Meghan Stronach from Qualcomm. In this post, we introduce an innovative solution for end-to-end model customization and deployment at the edge using Amazon SageMaker and Qualcomm AI Hub. This seamless cloud-to-edge AI development experience will enable developers to create
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Shared by AWS Machine Learning October 26, 2024
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
Shared by AWS Machine Learning October 28, 2024
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