AWS Machine Learning - AI

FEBRUARY 12, 2025

Large language models (LLMs) have revolutionized the field of natural language processing with their ability to understand and generate humanlike text. Researchers developed Medusa , a framework to speed up LLM inference by adding extra heads to predict multiple tokens simultaneously.

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AWS Machine Learning - AI

MARCH 4, 2025

To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. This allows teams to focus more on implementing improvements and optimizing AWS infrastructure. This scalability allows for more frequent and comprehensive reviews.

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AWS Machine Learning - AI

DECEMBER 4, 2024

At AWS re:Invent 2024, we are excited to introduce Amazon Bedrock Marketplace. This a revolutionary new capability within Amazon Bedrock that serves as a centralized hub for discovering, testing, and implementing foundation models (FMs).

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AWS Machine Learning - AI

NOVEMBER 7, 2024

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. It consists of one or more components depending on the number of FM providers and number and types of custom models used.

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AWS Machine Learning - AI

NOVEMBER 15, 2024

Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles. Principal also used the AWS open source repository Lex Web UI to build a frontend chat interface with Principal branding.

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AWS Machine Learning - AI

APRIL 10, 2025

The introduction of Amazon Nova models represent a significant advancement in the field of AI, offering new opportunities for large language model (LLM) optimization. In this post, we demonstrate how to effectively perform model customization and RAG with Amazon Nova models as a baseline.

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AWS Machine Learning - AI

OCTOBER 29, 2024

Refer to Supported Regions and models for batch inference for current supporting AWS Regions and models. Although batch inference offers numerous benefits, it’s limited to 10 batch inference jobs submitted per model per Region. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.

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AWS Machine Learning - AI

NOVEMBER 14, 2024

This is where AWS and generative AI can revolutionize the way we plan and prepare for our next adventure. With the significant developments in the field of generative AI , intelligent applications powered by foundation models (FMs) can help users map out an itinerary through an intuitive natural conversation interface.

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AWS Machine Learning - AI

DECEMBER 2, 2024

Today at AWS re:Invent 2024, we are excited to announce the new Container Caching capability in Amazon SageMaker, which significantly reduces the time required to scale generative AI models for inference. It supports a wide range of popular open source LLMs, making it a popular choice for diverse AI applications.

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AWS Machine Learning - AI

OCTOBER 16, 2024

As DPG Media grows, they need a more scalable way of capturing metadata that enhances the consumer experience on online video services and aids in understanding key content characteristics. The following were some initial challenges in automation: Language diversity – The services host both Dutch and English shows.

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Prisma Clud

NOVEMBER 22, 2024

Organizations are increasingly turning to cloud providers, like Amazon Web Services (AWS), to address these challenges and power their digital transformation initiatives. However, the vastness of AWS environments and the ease of spinning up new resources and services can lead to cloud sprawl and ongoing security risks.

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AWS Machine Learning - AI

NOVEMBER 1, 2024

This visibility is essential for setting accurate pricing for generative AI offerings, implementing chargebacks, and establishing usage-based billing models. Without a scalable approach to controlling costs, organizations risk unbudgeted usage and cost overruns. anthropic.claude-3-sonnet-20240229-v1:0", "inferenceProfileId": "us-1.anthropic.claude-3-sonnet-20240229-v1:0",

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AWS Machine Learning - AI

JUNE 18, 2024

Some examples include extracting players and positions in an NFL game summary, products mentioned in an AWS keynote transcript, or key names from an article on a favorite tech company. This process must be repeated for every new document and entity type, making it impractical for processing large volumes of documents at scale.

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AWS Machine Learning - AI

NOVEMBER 26, 2024

With the rise of large language models (LLMs) like Meta Llama 3.1, there is an increasing need for scalable, reliable, and cost-effective solutions to deploy and serve these models. Adjust the following configuration to suit your needs, such as the Amazon EKS version, cluster name, and AWS Region.

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AWS Machine Learning - AI

JULY 31, 2024

Amazon Web Services (AWS) is committed to supporting the development of cutting-edge generative artificial intelligence (AI) technologies by companies and organizations across the globe. Let’s dive in and explore how these organizations are transforming what’s possible with generative AI on AWS.

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AWS Machine Learning - AI

NOVEMBER 22, 2024

This post discusses how to use AWS Step Functions to efficiently coordinate multi-step generative AI workflows, such as parallelizing API calls to Amazon Bedrock to quickly gather answers to lists of submitted questions.

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AWS Machine Learning - AI

OCTOBER 29, 2024

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. This post provides guidance on how you can create a video insights and summarization engine using AWS AI/ML services.

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CIO

APRIL 15, 2025

During his one hour forty minute-keynote, Thomas Kurian, CEO of Google Cloud showcased updates around most of the companys offerings, including new large language models (LLMs) , a new AI accelerator chip, new open source frameworks around agents, and updates to its data analytics, databases, and productivity tools and services among others.

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AWS Machine Learning - AI

MARCH 27, 2025

Amazon Bedrock cross-Region inference capability that provides organizations with flexibility to access foundation models (FMs) across AWS Regions while maintaining optimal performance and availability. We provide practical examples for both SCP modifications and AWS Control Tower implementations.

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AWS Machine Learning - AI

DECEMBER 11, 2024

In this post, we illustrate how EBSCOlearning partnered with AWS Generative AI Innovation Center (GenAIIC) to use the power of generative AI in revolutionizing their learning assessment process. The evaluation process includes three phases: LLM-based guideline evaluation, rule-based checks, and a final evaluation.

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Dzone - DevOps

APRIL 9, 2025

Amazon Web Services (AWS) provides an expansive suite of tools to help developers build and manage serverless applications with ease. By abstracting the complexities of infrastructure, AWS enables teams to focus on innovation. Why Combine AI, ML, and Serverless Computing?

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AWS Machine Learning - AI

DECEMBER 11, 2024

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. Field Advisor serves four primary use cases: AWS-specific knowledge search With Amazon Q Business, weve made internal data sources as well as public AWS content available in Field Advisors index.

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AWS Machine Learning - AI

APRIL 16, 2025

Out-of-the-box models often lack the specific knowledge required for certain domains or organizational terminologies. To address this, businesses are turning to custom fine-tuned models, also known as domain-specific large language models (LLMs). Why LoRAX for LoRA deployment on AWS?

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AWS Machine Learning - AI

OCTOBER 31, 2024

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.

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AWS Machine Learning - AI

FEBRUARY 6, 2024

To support overarching pharmacovigilance activities, our pharmaceutical customers want to use the power of machine learning (ML) to automate the adverse event detection from various data sources, such as social media feeds, phone calls, emails, and handwritten notes, and trigger appropriate actions.

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AWS Machine Learning - AI

MARCH 13, 2025

DeepSeek-R1 , developed by AI startup DeepSeek AI , is an advanced large language model (LLM) distinguished by its innovative, multi-stage training process. Instead of relying solely on traditional pre-training and fine-tuning, DeepSeek-R1 integrates reinforcement learning to achieve more refined outputs.

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CIO

FEBRUARY 11, 2025

Add to this the escalating costs of maintaining legacy systems, which often act as bottlenecks for scalability. The latter option had emerged as a compelling solution, offering the promise of enhanced agility, reduced operational costs, and seamless scalability. Scalability. Scalability. Cost forecasting. The results?

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AWS Machine Learning - AI

MARCH 13, 2025

Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will further broaden their applications. Conversely, asynchronous event-driven systems offer greater flexibility and scalability through their distributed nature.

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Perficient

NOVEMBER 20, 2024

Introduction to Multiclass Text Classification with LLMs Multiclass text classification (MTC) is a natural language processing (NLP) task where text is categorized into multiple predefined categories or classes. Traditional approaches rely on training machine learning models, requiring labeled data and iterative fine-tuning.

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AWS Machine Learning - AI

JANUARY 29, 2025

Their DeepSeek-R1 models represent a family of large language models (LLMs) designed to handle a wide range of tasks, from code generation to general reasoning, while maintaining competitive performance and efficiency. For more information, see Create a service role for model import.

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CIO

NOVEMBER 1, 2024

As part of MMTech’s unifying strategy, Beswick chose to retire the data centers and form an “enterprisewide architecture organization” with a set of standards and base layers to develop applications and workloads that would run on the cloud, with AWS as the firm’s primary cloud provider. The biggest challenge is data.

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AWS Machine Learning - AI

APRIL 1, 2025

AWS App Studio is a generative AI-powered service that uses natural language to build business applications, empowering a new set of builders to create applications in minutes. Cross-instance Import and Export Enabling straightforward and self-service migration of App Studio applications across AWS Regions and AWS accounts.

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AWS Machine Learning - AI

MARCH 26, 2025

Use the us-west-2 AWS Region to run this demo. Prerequisites This notebook is designed to run on AWS, using Amazon Bedrock for both Anthropics Claude 3 Sonnet and Stability AI model access. Make sure you have the following set up before moving forward: An AWS account. An Amazon SageMaker domain.

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AWS Machine Learning - AI

MARCH 11, 2025

DeepSeek-R1 is a large language model (LLM) developed by DeepSeek AI that uses reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. See the following GitHub repo for more deployment examples using TGI, TensorRT-LLM, and Neuron.

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AWS Machine Learning - AI

MARCH 13, 2025

We discuss the unique challenges MaestroQA overcame and how they use AWS to build new features, drive customer insights, and improve operational inefficiencies. Amazon Bedrocks broad choice of FMs from leading AI companies, along with its scalability and security features, made it an ideal solution for MaestroQA.

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AWS Machine Learning - AI

MARCH 5, 2025

These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.

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AWS Machine Learning - AI

MARCH 20, 2025

Traditionally, transforming raw data into actionable intelligence has demanded significant engineering effort. It often requires managing multiple machine learning (ML) models, designing complex workflows, and integrating diverse data sources into production-ready formats.

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