AWS Machine Learning Blog

NOVEMBER 26, 2024

AWS Trainium and AWS Inferentia based instances, combined with Amazon Elastic Kubernetes Service (Amazon EKS), provide a performant and low cost framework to run LLMs efficiently in a containerized environment. For more information on how to view and increase your quotas, refer to Amazon EC2 service quotas.

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FEBRUARY 11, 2025

Amazon Q Business , a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprises systems. Then we introduce the solution deployment using three AWS CloudFormation templates.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

This wealth of content provides an opportunity to streamline access to information in a compliant and responsible way. 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.

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NOVEMBER 19, 2024

Amazon Bedrock offers a serverless experience so you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using AWS tools without having to manage infrastructure. This can lead to inefficiencies, delays, and errors, diminishing customer satisfaction.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

8B and 70B inference support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Trainium and Inferentia, enabled by the AWS Neuron software development kit (SDK), offer high performance and lower the cost of deploying Meta Llama 3.1 An AWS Identity and Access Management (IAM) role to access SageMaker.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Amazon SageMaker Studio is the first integrated development environment (IDE) purposefully designed to accelerate end-to-end machine learning (ML) development. These automations can greatly decrease overhead related to ML project setup, facilitate technical consistency, and save costs related to running idle instances.

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AWS Machine Learning Blog

DECEMBER 24, 2024

To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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APRIL 23, 2025

Retrieval Augmented Generation (RAG) applications have become increasingly popular due to their ability to enhance generative AI tasks with contextually relevant information. To assist in this effort, AWS provides a range of generative AI security strategies that you can use to create appropriate threat models.

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JANUARY 6, 2025

Precise), an Amazon Web Services (AWS) Partner , participated in the AWS Think Big for Small Business Program (TBSB) to expand their AWS capabilities and to grow their business in the public sector. The demand for modernization is growing, and Precise can help government agencies adopt AI/ML technologies.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

A reverse image search engine enables users to upload an image to find related information instead of using text-based queries. The AWS Command Line Interface (AWS CLI) installed on your machine to upload the dataset to Amazon S3. For more information on managing credentials securely, see the AWS Boto3 documentation.

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DECEMBER 3, 2024

Syngenta and AWS collaborated to develop Cropwise AI , an innovative solution powered by Amazon Bedrock Agents , to accelerate their sales reps’ ability to place Syngenta seed products with growers across North America. This post was written with Zach Marston and Serg Masis from Syngenta.

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AWS Machine Learning Blog

OCTOBER 22, 2024

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. For more information about this architecture, see New – Code Editor, based on Code-OSS VS Code Open Source now available in Amazon SageMaker Studio.

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OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Unstructured information may have a little or a lot of structure but in ways that are unexpected or inconsistent. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.

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AWS Machine Learning Blog

OCTOBER 16, 2024

The company developed an automated solution called Call Quality (CQ) using AI services from Amazon Web Services (AWS). In this post, we demonstrate how the CQ solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics.

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JANUARY 24, 2025

Use the AWS generative AI scoping framework to understand the specific mix of the shared responsibility for the security controls applicable to your application. The following figure of the AWS Generative AI Security Scoping Matrix summarizes the types of models for each scope.

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AWS Machine Learning Blog

FEBRUARY 26, 2025

ByteDance is a technology company that operates a range of content platforms to inform, educate, entertain, and inspire people across languages, cultures, and geographies. By using sophisticated ML algorithms, the platform efficiently scans billions of videos each day.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Using vLLM on AWS Trainium and Inferentia makes it possible to host LLMs for high performance inference and scalability. Deploy vLLM on AWS Trainium and Inferentia EC2 instances In these sections, you will be guided through using vLLM on an AWS Inferentia EC2 instance to deploy Meta’s newest Llama 3.2 You will use inf2.xlarge

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MARCH 21, 2025

AI for IT operations (AIOps) is the application of AI and machine learning (ML) technologies to automate and enhance IT operations. This post presents a comprehensive AIOps solution that combines various AWS services such as Amazon Bedrock , AWS Lambda , and Amazon CloudWatch to create an AI assistant for effective incident management.

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AWS Machine Learning Blog

APRIL 3, 2025

Theyre constantly seeking ways to use their vast amounts of information to gain competitive advantages. This post explores how OMRON Europe is using Amazon Web Services (AWS) to build its advanced ODAP and its progress toward harnessing the power of generative AI. Data is one of the most critical assets of many organizations.

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APRIL 23, 2025

Solution overview The NER & LLM Gen AI Application is a document processing solution built on AWS that combines NER and LLMs to automate document analysis at scale. Click here to open the AWS console and follow along. The endpoint lifecycle is orchestrated through dedicated AWS Lambda functions that handle creation and deletion.

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AWS Machine Learning Blog

MARCH 10, 2025

Investment professionals face the mounting challenge of processing vast amounts of data to make timely, informed decisions. This challenge is particularly acute in credit markets, where the complexity of information and the need for quick, accurate insights directly impacts investment outcomes.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

You can try out the models with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping provide data security.

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AWS Machine Learning Blog

OCTOBER 29, 2024

Refer to Supported Regions and models for batch inference for current supporting AWS Regions and models. To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

This new cutting-edge image generation model, which was trained on Amazon SageMaker HyperPod , empowers AWS customers to generate high-quality images from text descriptions with unprecedented ease, flexibility, and creative potential. Large model is available today in the following AWS Regions: US East (N. By adding Stable Diffusion 3.5

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AWS Machine Learning Blog

FEBRUARY 6, 2025

We show how to then prepare the fine-tuned model to run on AWS Inferentia2 powered Amazon EC2 Inf2 instances , unlocking superior price performance for your inference workloads. After the model is fine-tuned, you can compile and host the fine-tuned SDXL on Inf2 instances using the AWS Neuron SDK. An Amazon Web Services (AWS) account.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

By analyzing a wide range of data points, were able to quickly and accurately assess the risk associated with a loan, enabling us to make more informed lending decisions and get our clients the financing they need. Communication between the two systems was established through Kerberized Apache Livy (HTTPS) connections over AWS PrivateLink.

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DECEMBER 2, 2024

To learn more about the ModelBuilder class, refer to Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements. For more information on SchemaBuilder , refer to Define serialization and deserialization methods. Raghu Ramesha is an ML Solutions Architect with the Amazon SageMaker Service team.

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APRIL 24, 2025

This post describes a pattern that AWS and Cisco teams have developed and deployed that is viable at scale and addresses a broad set of challenging enterprise use cases. By focusing on the data domain of the input query, redundant information, such as schemas for other data domains in the enterprise data store, can be excluded.

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AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

The market size for multilingual content extraction and the gathering of relevant insights from unstructured documents (such as images, forms, and receipts) for information processing is rapidly increasing. In this stage, we store initial document information in an Amazon DynamoDB table after receiving an Amazon S3 event notification.

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APRIL 13, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses.

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AWS Machine Learning Blog

APRIL 29, 2025

At Data Reply and AWS, we are committed to helping organizations embrace the transformative opportunities generative AI presents, while fostering the safe, responsible, and trustworthy development of AI systems. These challenges manifest in two key ways: through inherent model vulnerabilities and adversarial threats.

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AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Your client applications invoke this endpoint to get inferences from the model.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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AWS Machine Learning Blog

MARCH 17, 2025

Their knowledge is static and confined to the information they were trained on, which becomes problematic when dealing with dynamic and constantly evolving domains like healthcare. Furthermore, healthcare decisions often require integrating information from multiple sources, such as medical literature, clinical databases, and patient records.

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AWS Machine Learning Blog

DECEMBER 23, 2024

Virtual Agent: Thats great, please say your 5 character booking reference, you will find it at the top of the information pack we sent. Virtual Agent: Thats great, please say your 5 character booking reference, you will find it at the top of the information pack we sent. Customer: Id like to check my booking. Please say yes or no.

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