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.

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AWS Machine Learning Blog

FEBRUARY 11, 2025

Although GraphStorm can run efficiently on single instances for small graphs, it truly shines when scaling to enterprise-level graphs in distributed mode using a cluster of Amazon Elastic Compute Cloud (Amazon EC2) instances or Amazon SageMaker. Today, AWS AI released GraphStorm v0.4. This dataset has approximately 170,000 nodes and 1.2

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AWS Machine Learning Blog

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. Organizations typically can’t predict their call patterns, so the solution relies on AWS serverless services to scale during busy times.

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AWS Machine Learning Blog

JULY 8, 2024

MLOps practitioners have many options to establish an MLOps platform; one among them is cloud-based integrated platforms that scale with data science teams. AWS provides a full-stack of services to establish an MLOps platform in the cloud that is customizable to your needs while reaping all the benefits of doing ML in the cloud.

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Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

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AWS Machine Learning Blog

DECEMBER 6, 2024

The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping to support data security. The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping to support data security. His area of focus is generative AI and AWS AI Accelerators.

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AWS Machine Learning Blog

APRIL 7, 2025

Virginia) AWS Region. Prerequisites To try the Llama 4 models in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker AI. Access to accelerated instances (GPUs) for hosting the LLMs.

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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

JUNE 20, 2024

AWS customers that implement secure development environments often have to restrict outbound and inbound internet traffic. Therefore, accessing AWS services without leaving the AWS network can be a secure workflow. Therefore, accessing AWS services without leaving the AWS network can be a secure workflow.

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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

SEPTEMBER 19, 2023

Technical challenges with multi-modal data further include the complexity of integrating and modeling different data types, the difficulty of combining data from multiple modalities (text, images, audio, video), and the need for advanced computer science skills and sophisticated analysis tools.

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AWS Machine Learning Blog

MARCH 18, 2025

With this launch, you can now deploy NVIDIAs optimized reranking and embedding models to build, experiment, and responsibly scale your generative AI ideas on AWS. As part of NVIDIA AI Enterprise available in AWS Marketplace , NIM is a set of user-friendly microservices designed to streamline and accelerate the deployment of generative AI.

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AWS Machine Learning Blog

DECEMBER 4, 2024

To access SageMaker Studio on the AWS Management Console , you need to set up an Amazon SageMaker domain. You also need an AWS Identity and Access Management (IAM) role with appropriate permissions. About the Author Vivek Gangasani is a Senior GenAI Specialist Solutions Architect at AWS.

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PyImageSearch

MAY 19, 2025

run_opensearch.sh Running OpenSearch Locally A script to start OpenSearch using Docker for local testing before deploying to AWS. Implement and analyze search results using Python scripts. Now, lets implement a Python script to execute the neural search query in OpenSearch. These can be used for evaluation and comparison.

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AWS Machine Learning Blog

DECEMBER 10, 2024

You can now use state-of-the-art model architectures, such as language models, computer vision models, and more, without having to build them from scratch. Prerequisites To try out Pixtral 12B in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources.

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AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Prerequisites To run this step-by-step guide, you need an AWS account with permissions to SageMaker, Amazon Elastic Container Registry (Amazon ECR), AWS Identity and Access Management (IAM), and AWS CodeBuild. Complete the following steps: Sign in to the AWS Management Console and open the IAM console. base-ubuntu18.04

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PyImageSearch

SEPTEMBER 30, 2024

Home Table of Contents Introduction to GitHub Actions for Python Projects Introduction What Is CICD? For Python projects, CI/CD pipelines ensure that your code is consistently integrated and delivered with high quality and reliability. Git is the most commonly used VCS for Python projects, enabling collaboration and version tracking.

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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, 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

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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AWS Machine Learning Blog

NOVEMBER 14, 2023

The solution also uses Amazon Bedrock , a fully managed service that makes foundation models (FMs) from Amazon and third-party model providers accessible through the AWS Management Console and APIs. Prerequisites For this tutorial, you need a bash terminal with Python 3.9 in computer science. - Dr. Liskov earned her Ph.D.

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AWS Machine Learning Blog

MARCH 14, 2025

The computer use agent demo powered by Amazon Bedrock Agents provides the following benefits: Secure execution environment Execution of computer use tools in a sandbox environment with limited access to the AWS ecosystem and the web. Prerequisites AWS Command Line Interface (CLI), follow instructions here. Require Node.js

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AWS Machine Learning Blog

NOVEMBER 22, 2024

Implementation details We spin up the cluster by calling the SageMaker control plane through APIs or the AWS Command Line Interface (AWS CLI) or using the SageMaker AWS SDK. In response, SageMaker spins up training jobs with the requested number and type of compute instances. 24xlarge compute instance.

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AWS Machine Learning Blog

MARCH 3, 2025

These recipes include a training stack validated by Amazon Web Services (AWS) , which removes the tedious work of experimenting with different model configurations, minimizing the time it takes for iterative evaluation and testing. Alternatively, you can also use AWS Systems Manager and run a command like the following to start the session.

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AWS Machine Learning Blog

APRIL 28, 2025

Amazon Nova models and Amazon Bedrock Amazon Nova models , unveiled at AWS re:Invent in December 2024, are optimized to deliver exceptional price-performance value, offering state-of-the-art performance on key text-understanding benchmarks at low cost. Choose us-east-1 as the AWS Region. gpus 2'] Ground truth pattern: python(3?)

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AWS Machine Learning Blog

FEBRUARY 11, 2025

Deploying a Falcon 3 model through SageMaker JumpStart offers two convenient approaches: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. About the authors Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science team at AWS.

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AWS Machine Learning Blog

AUGUST 7, 2023

In an effort to create and maintain a socially responsible gaming environment, AWS Professional Services was asked to build a mechanism that detects inappropriate language (toxic speech) within online gaming player interactions. Unfortunately, as in the real world, not all players communicate appropriately and respectfully.

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AWS Machine Learning Blog

JUNE 21, 2024

Solution overview Starting today, with SageMaker JumpStart and its private hub feature, administrators can create repositories for a subset of models tailored to different teams, use cases, or license requirements using the Amazon SageMaker Python SDK. For a list of filters you can apply, refer to SageMaker Python SDK.

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AWS Machine Learning Blog

MARCH 8, 2023

The workflow steps are as follows: Set up a SageMaker notebook and an AWS Identity and Access Management (IAM) role with appropriate permissions to allow SageMaker to access Amazon Elastic Container Registry (Amazon ECR), Secrets Manager, and other services within your AWS account. AWS Region Link us-east-1 (N.

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O'Reilly Media

NOVEMBER 26, 2024

Beyond the out-of-control cost, there is evidence that degrees do not map to the skills needed in today’s job market, and there’s an increasing disconnect—particularly in computer science—between the skills employers want and the skills colleges teach. We won’t name names, but we challenge you to do your own research.

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AWS Machine Learning Blog

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. We extract the default generic entities through the AWS SDK for Python (Boto3) as follows: import pandas as pd comprehend_client = boto3.client("comprehend")

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AWS Machine Learning Blog

MAY 31, 2024

You can discover and deploy the Falcon 2 11B model with a few clicks in Amazon SageMaker Studio or programmatically through the SageMaker Python SDK, enabling you to derive model performance and MLOps controls with SageMaker features such as Amazon SageMaker Pipelines , Amazon SageMaker Debugger , or container logs.

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AWS Machine Learning Blog

FEBRUARY 24, 2025

You can now discover and deploy Mistral models in Amazon SageMaker Studio or programmatically through the SageMaker Python SDK, enabling you to derive model performance and MLOps controls with Amazon SageMaker features such as Amazon SageMaker Pipelines , Amazon SageMaker Debugger , or container logs. Preston Tuggle is a Sr.

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AWS Machine Learning Blog

FEBRUARY 14, 2023

For instance, faculty in an educational institution belongs to different departments, and if a professor belonging to the computer science department signs in to the application and searches with the keywords “ faculty courses ,” then documents relevant to the same department come up as the top results, based on data source availability.

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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. In response, SageMaker launches training jobs with the requested number and type of compute instances to run specific tasks.

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AWS Machine Learning Blog

JUNE 25, 2024

The customer review analysis workflow consists of the following steps: A user uploads a file to dedicated data repository within your Amazon Simple Storage Service (Amazon S3) data lake, invoking the processing using AWS Step Functions. In the first step, an AWS Lambda function reads and validates the file, and extracts the raw data.

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