AWS Machine Learning Blog

MARCH 19, 2025

At Amazon Web Services (AWS), we recognize that many of our customers rely on the familiar Microsoft Office suite of applications, including Word, Excel, and Outlook, as the backbone of their daily workflows. Using AWS, organizations can host and serve Office Add-ins for users worldwide with minimal infrastructure overhead.

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AWS Machine Learning Blog

JANUARY 6, 2025

Prerequisites You should have the following prerequisites: An AWS account A SageMaker notebook instance An S3 bucket to store the input data Process the data To start, upload the log dataset to an S3 bucket in your AWS account. aws ecr get-login --region $region --registry-ids $account_id --no-include-email) !aws client("sts").get_caller_identity().get("Account")

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AWS Machine Learning Blog

FEBRUARY 11, 2025

Today, AWS AI released GraphStorm v0.4. Prerequisites To run this example, you will need an AWS account, an Amazon SageMaker Studio domain, and the necessary permissions to run BYOC SageMaker jobs. Using SageMaker Pipelines to train models provides several benefits, like reduced costs, auditability, and lineage tracking. million edges.

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AWS Machine Learning Blog

APRIL 7, 2025

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies and AWS. Solution overview The following diagram provides a high-level overview of AWS services and features through a sample use case. The response only cites sources that are relevant to the query.

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AWS Machine Learning Blog

JANUARY 29, 2025

In this post, we explore how to deploy distilled versions of DeepSeek-R1 with Amazon Bedrock Custom Model Import, making them accessible to organizations looking to use state-of-the-art AI capabilities within the secure and scalable AWS infrastructure at an effective cost. Watch this video demo for a step-by-step guide.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

Generate and run data transformation Python code. Stream 3: Generate and run data transformation Python code Next, we took the response from the API call and transformed it to answer the user question. A custom Python function verifies, formats, and invokes the API call, then passes the data in JSON format to the next step.

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AWS Machine Learning Blog

OCTOBER 21, 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. The import job can be invoked using the AWS Management Console or through APIs. Service access role.

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Architecting specific AWS Cloud solutions involves creating diagrams that show relationships and interactions between different services. Instead of building the code manually, you can use Anthropic’s Claude 3’s image analysis capabilities to generate AWS CloudFormation templates by passing an architecture diagram as input.

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AWS Machine Learning Blog

NOVEMBER 8, 2023

AWS and NVIDIA have come together to make this vision a reality. AWS, NVIDIA, and other partners build applications and solutions to make healthcare more accessible, affordable, and efficient by accelerating cloud connectivity of enterprise imaging. AHI provides API access to ImageSet metadata and ImageFrames.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

This required custom integration efforts, along with complex AWS Identity and Access Management (IAM) policy management, further complicating the model governance process. About the authors Ram Vittal is a Principal ML Solutions Architect at AWS. intended_uses="Not used except this test.", factors_affecting_model_efficiency="No.",

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AWS Machine Learning Blog

MAY 23, 2023

You can also access JumpStart models using the SageMaker Python SDK. In April 2023, AWS unveiled Amazon Bedrock , which provides a way to build generative AI-powered apps via pre-trained models from startups including AI21 Labs , Anthropic , and Stability AI. Clone and set up the AWS CDK application.

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AWS Machine Learning Blog

OCTOBER 10, 2023

Customers often need to train a model with data from different regions, organizations, or AWS accounts. Existing partner open-source FL solutions on AWS include FedML and NVIDIA FLARE. These open-source packages are deployed in the cloud by running in virtual machines, without using the cloud-native services available on AWS.

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AWS Machine Learning Blog

JULY 26, 2023

Generative AI Foundations on AWS is a new technical deep dive course that gives you the conceptual fundamentals, practical advice, and hands-on guidance to pre-train, fine-tune, and deploy state-of-the-art foundation models on AWS and beyond. Feel free to reach out to me on Medium, LinkedIn , GitHub , or through your AWS teams.

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AWS Machine Learning Blog

MAY 30, 2024

Because Amazon Bedrock is serverless, you don’t have to manage infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. AWS Prototyping developed an AWS Cloud Development Kit (AWS CDK) stack for deployment following AWS best practices.

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AWS Machine Learning Blog

MAY 28, 2025

This integrated approach enables organizations to efficiently process, analyze, and derive insights from diverse content formats while using a robust and scalable infrastructure deployed using the AWS Cloud Development Kit (AWS CDK). See Getting Started With the AWS CDK for additional details and prerequisites.

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AWS Machine Learning Blog

AUGUST 30, 2023

This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. In the following sections, we demonstrate fine-tuning an LLM available on SageMaker JumpStart for summarization of a domain-specific task via the SageMaker Python SDK. We use an ml.t3.medium

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Flipboard

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. AWS solution architecture In this section, we illustrate how you might implement the architecture on AWS. The demo code is available in the GitHub repository.

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Flipboard

MARCH 31, 2025

The following illustration describes the components of an agentic AI system: Overview of CrewAI CrewAI is an enterprise suite that includes a Python-based open source framework. Scalability and reliability backed by AWS infrastructure This means your agent systems can handle increasing workloads while maintaining consistent performance.

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Flipboard

MAY 1, 2025

FastMCP is used for rapid prototyping, educational demos, and scenarios where development speed is a priority. By doing this, clients and servers can scale independently, making it a great fit for serverless orchestration powered by Lambda, AWS Fargate for Amazon ECS, or Fargate for Amazon EKS.

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Flipboard

APRIL 1, 2025

To improve factual accuracy of large language model (LLM) responses, AWS announced Amazon Bedrock Automated Reasoning checks (in gated preview) at AWS re:Invent 2024. For example, AWS customers have direct access to automated reasoning-based features such as IAM Access Analyzer , S3 Block Public Access , or VPC Reachability Analyzer.

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AWS Machine Learning Blog

MARCH 14, 2025

In this post, we create a computer use agent demo that provides the critical orchestration layer that transforms computer use from a perception capability into actionable automation. You can recreate this example in the us-west-2 AWS Region with the AWS Cloud Development Kit (AWS CDK) by following the instructions in the GitHub repository.

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AWS Machine Learning Blog

SEPTEMBER 15, 2023

In this post, we introduce LLM agents and demonstrate how to build and deploy an e-commerce LLM agent using Amazon SageMaker JumpStart and AWS Lambda. To power the LLM agent, we use a Flan-UL2 model deployed as a SageMaker endpoint and use data retrieval tools built with AWS Lambda.

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AWS Machine Learning Blog

APRIL 7, 2023

To reduce the barrier to entry of ML at the edge, we wanted to demonstrate an example of deploying a pre-trained model from Amazon SageMaker to AWS Wavelength , all in less than 100 lines of code. In this post, we demonstrate how to deploy a SageMaker model to AWS Wavelength to reduce model inference latency for 5G network-based applications.

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AWS Machine Learning Blog

MAY 1, 2025

In this post, we show how you can automate language localization through translating documents using Amazon Web Services (AWS). The solution combines Amazon Bedrock and AWS Serverless technologies , a suite of fully managed event-driven services for running code, managing data, and integrating applicationsall without managing servers.

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AWS Machine Learning Blog

MAY 14, 2025

You can execute each step in the training pipeline by initiating the process through the SageMaker control plane using APIs, AWS Command Line Interface (AWS CLI), or the SageMaker ModelTrainer SDK. Alternatively, you can also use AWS Systems Manager and run a command such as the following to start the session.

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Flipboard

DECEMBER 11, 2024

Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. Youre redirected to the AWS CloudFormation console to deploy a stack to configure VPC resources.

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AWS Machine Learning Blog

JULY 26, 2023

In this post, we show how you can run Stable Diffusion models and achieve high performance at the lowest cost in Amazon Elastic Compute Cloud (Amazon EC2) using Amazon EC2 Inf2 instances powered by AWS Inferentia2. versions on AWS Inferentia2 cost-effectively. You can run both Stable Diffusion 2.1 The Stable Diffusion 2.1

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AWS Machine Learning Blog

NOVEMBER 7, 2023

Today, we are excited to unveil three generative AI demos, licensed under MIT-0 license : Amazon Kendra with foundational LLM – Utilizes the deep search capabilities of Amazon Kendra combined with the expansive knowledge of LLMs. Having the right setup in place is the first step towards a seamless deployment of the demos. Python 3.6

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AWS Machine Learning Blog

FEBRUARY 18, 2025

Recognizing this challenge as an opportunity for innovation, F1 partnered with Amazon Web Services (AWS) to develop an AI-driven solution using Amazon Bedrock to streamline issue resolution. The objective was to use AWS to replicate and automate the current manual troubleshooting process for two candidate systems.

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AWS Machine Learning Blog

AUGUST 26, 2024

AWS makes it possible for organizations of all sizes and developers of all skill levels to build and scale generative AI applications with security, privacy, and responsible AI. In this post, we dive into the architecture and implementation details of GenASL, which uses AWS generative AI capabilities to create human-like ASL avatar videos.

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AWS Machine Learning Blog

OCTOBER 21, 2024

However, customers who want to deploy LLMs in their own self-managed workflows for greater control and flexibility of underlying resources can use these LLMs optimized on top of AWS Inferentia2-powered Amazon Elastic Compute Cloud (Amazon EC2) Inf2 instances. Main components The following are the main components of the solution.

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Flipboard

NOVEMBER 30, 2023

Although it provides various entry points like the SageMaker Python SDK, AWS SDKs, the SageMaker console, and Amazon SageMaker Studio notebooks to simplify the process of training and deploying ML models at scale, customers are still looking for better ways to deploy their models for playground testing and to optimize production deployments.

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AWS Machine Learning Blog

APRIL 11, 2023

With Streamlit , developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. As an example, we use a custom Amazon Rekognition demo, which will annotate and label an uploaded image. Create Studio using JupyterLab 3.0

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AWS Machine Learning Blog

AUGUST 1, 2023

With the power of state-of-the-art techniques, the creative agency can support their customer by using generative AI models within their secure AWS environment. AWS has also developed hardware and chips using AWS Inferentia2 for high performance at the lowest cost for generative AI inference.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

The task involved writing Python code to read data, transform it, and then visualize it in an interesting map. Prerequisites There are a few prerequisites to deploy the demo. You’ll need access to an AWS account with an access key or AWS Identity and Access Management (IAM) role with permissions to Amazon Bedrock and Amazon Location.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

We use Streamlit for the sample demo application UI. In terms of security, both the input and output are secured using TLS using AWS Sigv4 Auth. Prerequisites You need an AWS account with an AWS Identity and Access Management (IAM) role with permissions to manage resources created as part of the solution.

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AWS Machine Learning Blog

JUNE 11, 2024

Such a flow can run in each needed AWS Region supported by Amazon Bedrock to address any compliance needs of their customers. In addition, to secure the usage of Amazon Bedrock with least privilege, Wiz uses AWS permission sets and follows AWS best practices. Learn more about Wiz and check out a live demo.

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