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

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Customers often need to train a model with data from different regions, organizations, or AWS accounts. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets.

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AWS Machine Learning Blog

APRIL 16, 2024

It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language. Version control – With AWS CloudFormation, you can use version control systems like Git to manage your CloudFormation templates.

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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

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

OCTOBER 23, 2024

The following demo shows Agent Creator in action. SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). To address customers’ requirements about data privacy and sovereignty, SnapLogic deploys the data plane within the customer’s VPC on AWS.

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AWS Machine Learning Blog

APRIL 22, 2025

To address these challenges, Infosys partnered with Amazon Web Services (AWS) to develop the Infosys Event AI to unlock the insights generated during events. The services used in the solution are granted least-privilege permissions through AWS Identity and Access Management (IAM) policies for security purposes.

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Dataconomy

JUNE 16, 2023

The cloud-based DLP solution from Gamma AI uses cutting-edge deep learning for contextual perception to achieve a data classification accuracy of 99.5%. For a free initial consultation call, you can email sales@gammanet.com or click “Request a Demo” on the Gamma website ([link] Go to the Gamma.AI

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Flipboard

FEBRUARY 7, 2023

Although you can easily carry out smaller experiments and demos with the sample notebooks presented in this post on Studio Lab for free, it is recommended to use Amazon SageMaker Studio when you train your own medical image models at scale.

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Data Science Dojo

NOVEMBER 15, 2023

The conference will feature a wide range of sessions, including keynotes, panels, workshops, and demos. The AI Expo features a variety of talks, workshops, and demos on a wide range of AI topics. The AI Expo is a great opportunity to learn from experts from companies like AWS, IBM, etc.

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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

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

APRIL 5, 2024

It’s based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images and videos daily. It requires no machine learning (ML) expertise to use and we’re continually adding new computer vision features to the service. session.Session().region_name

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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

MARCH 20, 2025

Our partnership with AWS and our commitment to be early adopters of innovative technologies like Amazon Bedrock underscore our dedication to making advanced HCM technology accessible for businesses of any size. We are thrilled to partner with AWS on this groundbreaking generative AI project. John Canada, VP of Engineering at Asure.

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Flipboard

APRIL 16, 2025

LLMs are large deep learning models that are pre-trained on vast amounts of data. The solution uses various AWS services to create an end-to-end system that enables field technicians to capture label images, extract data using AI models, verify the accuracy, and seamlessly update the inventory database.

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AWS Machine Learning Blog

APRIL 19, 2024

We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. They are also supported by AWS CloudFormation. Release v1.2.9

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AWS Machine Learning Blog

FEBRUARY 19, 2024

DJL Serving is built on top of DJL , a deep learning library written in the Java programming language. It can take a deep learning model, several models, or workflows and make them available through an HTTP endpoint. example in this demo can be seen in the GitHub repo. The full model.py We create a model.tar.gz

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AWS Machine Learning Blog

MARCH 20, 2023

In the context of deep learning, the predominant numerical format used for research and deployment has so far been 32-bit floating point, or FP32. However, the need for reduced bandwidth and compute requirements of deep learning models has driven research into using lower-precision numerical formats.

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

This feature is particularly beneficial for deep learning and generative AI models that require accelerated compute. The cost savings achieved through resource sharing and simplified model management makes SageMaker MMEs an excellent choice for you to host models at scale on AWS. RUN pip install opencv-python-headless==4.7.0.68

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. Business requirements We are the US squad of the Sportradar AI department. The architecture of DJL is engine agnostic.

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AWS Machine Learning Blog

JANUARY 31, 2024

These models are based on deep learning architectures such as Transformers, which can capture the contextual information and relationships between words in a sentence more effectively. You can use it via either the Amazon Bedrock REST API or the AWS SDK. Why do we need an embeddings model? Nitin Eusebius is a Sr.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Prerequisites To continue with the examples in this post, you need to create the required AWS resources.

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AWS Machine Learning Blog

DECEMBER 14, 2023

To reduce the effort of preparing training data, we built a pre-labeling tool using AWS Step Functions that automatically pre-annotates documents by using existing tabular entity data. For the demo, we use simulated bank statements like the following example. The first technique is fuzzy matching.

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AWS Machine Learning Blog

OCTOBER 22, 2024

In this post, we explore how organizations can address these challenges and cost-effectively customize and adapt FMs using AWS managed services such as Amazon SageMaker training jobs and Amazon SageMaker HyperPod. The following demo provides a high-level, step-by-step guide to using Amazon SageMaker training jobs.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

Working with the AWS Generative AI Innovation Center , DoorDash built a solution to provide Dashers with a low-latency self-service voice experience to answer frequently asked questions, reducing the need for live agent assistance, in just 2 months. “We You can deploy the solution in your own AWS account and try the example solution.

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AWS Machine Learning Blog

JULY 5, 2023

What if we could apply deep learning techniques to common areas that drive vehicle failures, unplanned downtime, and repair costs? Solution overview The AWS predictive maintenance solution for automotive fleets applies deep learning techniques to common areas that drive vehicle failures, unplanned downtime, and repair costs.

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AWS Machine Learning Blog

AUGUST 3, 2023

Computer vision (CV) is one of the most common applications of machine learning (ML) and deep learning. We demonstrate how you can combine well-known ML solutions with postprocessing to address this problem on the AWS Cloud. We use deep learning models to solve this problem.

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AWS Machine Learning Blog

AUGUST 24, 2023

Today, we’re pleased to announce the preview of Amazon SageMaker Profiler , a capability of Amazon SageMaker that provides a detailed view into the AWS compute resources provisioned during training deep learning models on SageMaker. Framework Version AWS DLC Image URI PyTorch 2.0.0 gpu-py310-cu118-ubuntu20.04-sagemaker",

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AWS Machine Learning Blog

OCTOBER 17, 2023

The Amazon Personalize Search Ranking plugin within OpenSearch Service allows you to improve the end-user engagement and conversion from your website and app search by taking advantage of the deep learning capabilities offered by Amazon Personalize. Technical Product Manager working with AWS AI/ML on the Amazon Personalize team.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. You can also use the StartIngestionJob API to trigger the sync via the AWS SDK.

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AWS Machine Learning Blog

AUGUST 9, 2023

To scale the proposed solution for production and streamline the deployment of AI models in the AWS environment, we demonstrate it using SageMaker endpoints. Prerequisites We have developed an AWS CloudFormation template that will create the SageMaker notebooks used to deploy the endpoints and run inference.

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AWS Machine Learning Blog

MAY 13, 2024

Since then, Amazon Web Services (AWS) has introduced new services such as Amazon Bedrock. You can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. It’s serverless, so you don’t have to manage any infrastructure.

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AWS Machine Learning Blog

JUNE 28, 2023

In this post, we explore using AWS AI services Amazon Rekognition and Amazon Comprehend , along with other techniques, to effectively moderate Stable Diffusion model-generated content in near-real time. The demo app blurs the actual generated image if it contains unsafe content. We tested the app with the sample prompt “A sexy lady.”

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AWS Machine Learning Blog

APRIL 17, 2023

However, as the size and complexity of the deep learning models that power generative AI continue to grow, deployment can be a challenging task. Then, we highlight how Amazon SageMaker large model inference deep learning containers (LMI DLCs) can help with optimization and deployment.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

The demo implementation code is available in the following GitHub repo. Utilizing the latest Hugging Face LLM modules on Amazon SageMaker, AWS customers can now tap into the power of SageMaker deep learning containers (DLCs). About the authors Alfred Shen is a Senior AI/ML Specialist at AWS.

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AWS Machine Learning Blog

AUGUST 14, 2023

SageMaker JumpStart SageMaker JumpStart serves as a model hub encapsulating a broad array of deep learning models for text, vision, audio, and embedding use cases. With over 500 models, its model hub comprises both public and proprietary models from AWS’s partners such as AI21, Stability AI, Cohere, and LightOn.

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