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

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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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

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

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

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

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

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

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

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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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

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

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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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

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

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

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

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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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

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

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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AWS Machine Learning Blog

NOVEMBER 22, 2023

The IDP Well-Architected Custom Lens is intended for all AWS customers who use AWS to run intelligent document processing (IDP) solutions and are searching for guidance on how to build a secure, efficient, and reliable IDP solution on AWS.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. While knowing Python, R, and SQL are expected, you’ll need to go beyond that.

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AWS Machine Learning Blog

DECEMBER 13, 2023

The built-in project templates provided by Amazon SageMaker include integration with some of third-party tools, such as Jenkins for orchestration and GitHub for source control, and several utilize AWS native CI/CD tools such as AWS CodeCommit , AWS CodePipeline , and AWS CodeBuild. An AWS account.

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AWS Machine Learning Blog

MARCH 28, 2024

For more information on Mixtral-8x7B Instruct on AWS, refer to Mixtral-8x7B is now available in Amazon SageMaker JumpStart. LangChain is an open source Python library designed to build applications with LLMs. Before you get started with the solution, create an AWS account. This identity is called the AWS account root user.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

LangChain is a Python library designed to build applications with LLMs. Prerequisites To implement this solution, you need the following: An AWS account with privileges to create AWS Identity and Access Management (IAM) roles and policies. Basic familiarity with SageMaker and AWS services that support LLMs. Python 3.10

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AWS Machine Learning Blog

FEBRUARY 16, 2024

The models excel in Python, C++, Java, PHP, C#, TypeScript, and Bash, and have the potential to save developers’ time and make software workflows more efficient. Because the models are hosted and deployed on AWS, you can rest assured that your data, whether used for evaluating or using the model at scale, is never shared with third parties.

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AWS Machine Learning Blog

APRIL 29, 2025

Prompt optimization streamlines the way that AWS developers interact with FMs on Amazon Bedrock, automatically adapts the prompts to the selected models, and generates for better performance. In this work, we use the DSPy (Declarative Self-improving Python) optimizer for the data-aware optimization.

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Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

In terms of security, both the input and output are secured using TLS using AWS Sigv4 Auth. In this post, we showcase two container options to create a SageMaker endpoint with response streaming: using an AWS Large Model Inference (LMI) and Hugging Face Text Generation Inference (TGI) container.

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AWS Machine Learning Blog

APRIL 25, 2023

With this new capability of the SageMaker Python SDK, data scientists can onboard their ML code to the SageMaker Training platform in a few minutes. In this release, you can run your local machine learning (ML) Python code as a single-node Amazon SageMaker training job or multiple parallel jobs.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The AWS SDK gives you most control and flexibility. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python. The SageMaker Python SDK is a high-level Python API that abstracts some of the steps and configuration, and makes it easier to deploy models.

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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

NOVEMBER 7, 2023

These demos can be seamlessly deployed in your AWS account, offering foundational insights and guidance on utilizing AWS services to create a state-of-the-art LLM generative AI question and answer bot and content generation. Prerequisites You must have the following prerequisites: An AWS account. Python 3.6 x or later.

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AWS Machine Learning Blog

APRIL 26, 2024

You can now discover and deploy DBRX models 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 Amazon SageMaker features such as Amazon SageMaker Pipelines , Amazon SageMaker Debugger , or container logs.

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