AWS Machine Learning Blog

NOVEMBER 13, 2024

Extraction – A document schema definition is used to formulate the prompt and documents are embedded into the prompt and sent to Amazon Bedrock for extraction. We specifically used the Rhubarb Python framework to extract JSON schema -based data from the documents. Results are stored as JSON in a folder in Amazon S3.

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Flipboard

JUNE 30, 2025

To address this need, we are introducing a new capability in the SageMaker Python SDK that revolutionizes how you build and deploy inference workflows on SageMaker. In this post, we provide an overview of the user experience, detailing how to set up and deploy these workflows with multiple models using the SageMaker Python SDK.

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AWS Machine Learning Blog

MAY 31, 2023

In late 2022, AWS announced the general availability of Amazon EC2 Trn1 instances powered by AWS Trainium accelerators, which are purpose built for high-performance deep learning training. Solution overview We walk you through the following high-level steps: Provision an ECS cluster of Trn1 instances with AWS CloudFormation.

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AWS Machine Learning Blog

JUNE 26, 2025

We will use a customer review analysis example to demonstrate how Bedrock generates structured outputs, such as sentiment scores, with simplified Python code. This method supports standard JSON schema integration directly into tool definitions, facilitating output alignment with predefined formats.

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AWS Machine Learning Blog

MARCH 15, 2023

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. See the following code: sm-docker build.

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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

SEPTEMBER 4, 2024

A challenge for DevOps engineers is the additional complexity that comes from using Kubernetes to manage the deployment stage while resorting to other tools (such as the AWS SDK or AWS CloudFormation ) to manage the model building pipeline. This entire workflow is shown in the following solution diagram. yq for YAML processing.

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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

JANUARY 27, 2025

In such cases, SageMaker allows you to extend its functionality by creating custom container images and defining custom model definitions. Write a Python model definition using the SageMaker inference.py For this post, we use the us-east-1 AWS Region: Have access to a POSIX based (Mac/Linux) system or SageMaker notebooks.

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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

OCTOBER 24, 2024

These agents work with AWS managed infrastructure capabilities and Amazon Bedrock , reducing infrastructure management overhead. The agent can recommend software and architecture design best practices using the AWS Well-Architected Framework for the overall system design. What are the top five most expensive products?

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Flipboard

JUNE 24, 2025

For AWS, Boto3 provides Python bindings to AWS services, including Amazon Bedrock, which provides access to a number of FMs. Prerequisites To follow this tutorial, you must have the following: An AWS account with access to Amazon Bedrock. Phoenix can also be self-hosted on AWS instead. An Amazon Bedrock agent.

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AWS Machine Learning Blog

MAY 6, 2025

Overview Custom metrics in Amazon Bedrock Evaluations offer the following features: Simplified getting started experience Pre-built starter templates are available on the AWS Management Console based on our industry-tested built-in metrics, with options to create from scratch for specific evaluation criteria. bedrock_client = boto3.client('bedrock',

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Flipboard

FEBRUARY 10, 2025

Solution overview CrewAI provides a robust framework for developing multi-agent systems that integrate with AWS services, particularly SageMaker AI. Having access to a JupyterLab IDE with Python 3.9, To get started, complete the following steps: Install the latest version of the sagemaker-python-sdk using pip. 3.10, or 3.11

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

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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

SEPTEMBER 18, 2024

In addition to its groundbreaking AI innovations, Zeta Global has harnessed Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to deploy a multitude of smaller models efficiently. Airflow for workflow orchestration Airflow schedules and manages complex workflows, defining tasks and dependencies in Python code.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

Prerequisites Before you start, make sure you have the following prerequisites in place: Create an AWS account , or sign in to your existing account. Make sure that you have the correct AWS Identity and Access Management (IAM) permissions to use Amazon Bedrock. Install Python 3.8 Install Python 3.8 Install pip.

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Flipboard

MAY 1, 2025

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. Lets start with the MCP server definition. To create an MCP server, we use the official Model Context Protocol Python SDK.

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AWS Machine Learning Blog

OCTOBER 26, 2023

Our innovative new A-POPs (or vending machines) deliver enhanced customer experiences at ten times lower cost because of the performance and cost advantages AWS Inferentia delivers. Unlocking high-performance and cost-effective inference using AWS Inferentia As retailers look to scale operations, cost of A-POPs becomes a consideration.

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AWS Machine Learning Blog

MAY 8, 2023

In a previous post , we discussed MLflow and how it can run on AWS and be integrated with SageMaker—in particular, when tracking training jobs as experiments and deploying a model registered in MLflow to the SageMaker managed infrastructure. The changes to the MLflow Python SDK are available for everyone since MLflow version 1.30.0.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity AI are going all in on AWS for generative AI.

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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

MAY 9, 2023

In this post, we help you understand the Python backend that is supported by Triton on SageMaker so that you can make an informed decision for your workloads and achieve great results. Triton supports instance types that support GPUs, CPUs, and AWS Inferentia chips, which allow you to maximize the performance for your workloads.

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DrivenData Labs

MAY 12, 2025

After the challenge, the research team at NOAA and NCEI worked with one of the winners to implement an ensemble of the top two models, incorporating into NOAA's High Definition Geomagnetic Model (HDGM) and making the predictions publicly available in real-time. The full video can be viewed on the website.

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AWS Machine Learning Blog

JUNE 26, 2025

Solution overview Until now, when using Amazon Bedrock Guardrails, you were provided with a single set of the safeguards associated to specific AWS Regions and a limited set of languages supported. Configure safeguard tiers using the AWS SDK You can also configure the guardrail’s tiers using the AWS SDK.

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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

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 Python 3.11

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AWS Machine Learning Blog

APRIL 2, 2025

Ray is an open source framework that makes it straightforward to create, deploy, and optimize distributed Python jobs. Ray is an open-source distributed computing framework designed to run highly scalable and parallel Python applications. We primarily focus on ML training use cases.

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AWS Machine Learning Blog

JUNE 4, 2024

Terraform is an IaC tool that allows you to manage AWS resources, software as a service (SaaS) resources, datasets, and more, using declarative configuration. Configure your local Python virtual environment. Prerequisites This solution requires the following prerequisites: An AWS account. python v3.8 Terraform v1.0.0

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AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of relying on predefined, rigid definitions, our approach follows the principle of understanding a set. Its important to note that the learned definitions might differ from common expectations. Instead of relying solely on compressed definitions, we provide the model with a quasi-definition by extension.

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AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. or later image versions. or later image versions.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

We’ll cover how technologies such as Amazon Textract, AWS Lambda , Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service can be integrated into a workflow that seamlessly processes documents. The main concepts used are the AWS Cloud Development Kit (CDK) constructs, the actual CDK stacks and AWS Step Functions.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

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AWS Machine Learning Blog

APRIL 25, 2024

In AWS, the FMEval library within Amazon SageMaker Clarify streamlines the evaluation and selection of foundation models (FMs) for tasks like text summarization, question answering, and classification. To learn more about FMEval in AWS and how to use it effectively, refer to Use SageMaker Clarify to evaluate large language models.

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AWS Machine Learning Blog

AUGUST 22, 2024

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators. The SageMaker Pipelines decorator feature helps convert local ML code written as a Python program into one or more pipeline steps.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

Prerequisites To build the solution yourself, there are the following prerequisites: You need an AWS account with an AWS Identity and Access Management (IAM) role that has permissions to manage resources created as part of the solution (for example AmazonSageMakerFullAccess and AmazonS3FullAccess ).

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