AWS Machine Learning Blog

NOVEMBER 22, 2024

Because Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. client( service_name="bedrock-runtime", region_name="us-east-1" ) Define the model to invoke using its model ID.

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How to Learn Machine Learning

DECEMBER 24, 2024

If you’re diving into the world of machine learning, AWS Machine Learning provides a robust and accessible platform to turn your data science dreams into reality. Introduction Machine learning can seem overwhelming at first – from choosing the right algorithms to setting up infrastructure.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

In 2018, I sat in the audience at AWS re:Invent as Andy Jassy announced AWS DeepRacer —a fully autonomous 1/18th scale race car driven by reinforcement learning. At the time, I knew little about AI or machine learning (ML). seconds, securing the 2018 AWS DeepRacer grand champion title!

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Data Science Dojo

OCTOBER 31, 2024

Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Applied Machine Learning Scientist Description : Applied ML Scientists focus on translating algorithms into scalable, real-world applications.

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Flipboard

JUNE 5, 2025

Amazon SageMaker AI provides a fully managed service for deploying these machine learning (ML) models with multiple inference options, allowing organizations to optimize for cost, latency, and throughput. AWS has always provided customers with choice. That includes model choice, hardware choice, and tooling choice.

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AWS Machine Learning Blog

DECEMBER 24, 2024

To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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AWS Machine Learning Blog

NOVEMBER 19, 2024

The excitement is building for the fourteenth edition of AWS re:Invent, and as always, Las Vegas is set to host this spectacular event. Third, we’ll explore the robust infrastructure services from AWS powering AI innovation, featuring Amazon SageMaker , AWS Trainium , and AWS Inferentia under AI/ML, as well as Compute topics.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

In this post, we explore how to use the power of AWS Resilience Hub and Amazon Bedrock to bridge this gap and streamline the process of sharing architectural findings across your organization. Prerequisites For this walkthrough, the following are required: An AWS account. AWS Management Console access. A Python 3.12

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AWS Machine Learning Blog

JANUARY 7, 2025

The solution proposed in this post relies on LLMs context learning capabilities and prompt engineering. It enables you to use an off-the-shelf model as is without involving machine learning operations (MLOps) activity. To run the project code, make sure that you have fulfilled the AWS CDK prerequisites for Python.

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Flipboard

DECEMBER 3, 2024

Syngenta and AWS collaborated to develop Cropwise AI , an innovative solution powered by Amazon Bedrock Agents , to accelerate their sales reps’ ability to place Syngenta seed products with growers across North America. The collaboration between Syngenta and AWS showcases the transformative power of LLMs and AI agents.

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Analytics Vidhya

MAY 20, 2022

Introduction Most data science projects deploy machine learning models as an on-demand prediction service or in batch prediction mode. The post Creating an ML Web App and Deploying it on AWS appeared first on Analytics Vidhya. Some modern applications deploy embedded models in edge and mobile devices.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

8B and 70B inference support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Trainium and Inferentia, enabled by the AWS Neuron software development kit (SDK), offer high performance and lower the cost of deploying Meta Llama 3.1 An AWS Identity and Access Management (IAM) role to access SageMaker.

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AWS Machine Learning Blog

DECEMBER 9, 2024

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Amazon SageMaker Studio is the first integrated development environment (IDE) purposefully designed to accelerate end-to-end machine learning (ML) development. The AWS CDK is a framework for defining cloud infrastructure as code. Both are deployed and managed with AWS CDK custom resources. The AWS CDK installed.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

With the current demand for AI and machine learning (AI/ML) solutions, the processes to train and deploy models and scale inference are crucial to business success. Even though AI/ML and especially generative AI progress is rapid, machine learning operations (MLOps) tooling is continuously evolving to keep pace.

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AWS Machine Learning Blog

APRIL 1, 2025

Were excited to announce the open source release of AWS MCP Servers for code assistants a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. This post is the first in a series covering AWS MCP Servers.

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AWS Machine Learning Blog

OCTOBER 15, 2024

We’re excited to announce the release of SageMaker Core , a new Python SDK from Amazon SageMaker designed to offer an object-oriented approach for managing the machine learning (ML) lifecycle. The SageMaker Core SDK comes bundled as part of the SageMaker Python SDK version 2.231.0

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AWS Machine Learning Blog

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. By using a combination of AWS services, you can implement this feature effectively, overcoming the current limitations within SageMaker.

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AWS Machine Learning Blog

OCTOBER 31, 2024

By integrating human annotators with machine learning, SageMaker Ground Truth significantly reduces the cost and time required for data labeling. Create the labeling job using the CreateLabelingJob API You can also create the custom labeling job programmatically by using the AWS SDK to invoke the CreateLabelingJob API.

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AWS Machine Learning Blog

OCTOBER 31, 2024

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.

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AWS Machine Learning Blog

JANUARY 7, 2025

To address this need, AWS generative AI best practices framework was launched within AWS Audit Manager , enabling auditing and monitoring of generative AI applications. Figure 1 depicts the systems functionalities and AWS services. Select AWS Generative AI Best Practices Framework for assessment. Choose Create assessment.

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Flipboard

DECEMBER 2, 2024

We explore two approaches: using the SageMaker Python SDK for programmatic implementation, and using the Amazon SageMaker Studio UI for a more visual, interactive experience. In this post, we walked through the step-by-step process of implementing this feature through both the SageMaker Python SDK and SageMaker Studio UI.

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Flipboard

NOVEMBER 21, 2023

MATLAB   is a popular programming tool for a wide range of applications, such as data processing, parallel computing, automation, simulation, machine learning, and artificial intelligence. In recent years, MathWorks has brought many product offerings into the cloud, especially on Amazon Web Services (AWS).

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Dataconomy

APRIL 28, 2023

Machine learning deployment is a crucial step in bringing the benefits of data science to real-world applications. With the increasing demand for machine learning deployment, various tools and platforms have emerged to help data scientists and developers deploy their models quickly and efficiently.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Using vLLM on AWS Trainium and Inferentia makes it possible to host LLMs for high performance inference and scalability. Deploy vLLM on AWS Trainium and Inferentia EC2 instances In these sections, you will be guided through using vLLM on an AWS Inferentia EC2 instance to deploy Meta’s newest Llama 3.2 You will use inf2.xlarge

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Streamlit is an open source framework for data scientists to efficiently create interactive web-based data applications in pure Python. Prerequisites To perform this solution, complete the following: Create and activate an AWS account. Make sure your AWS credentials are configured correctly. Install Python 3.7

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AWS Machine Learning Blog

DECEMBER 12, 2024

Amazon SageMaker has redesigned its Python SDK to provide a unified object-oriented interface that makes it straightforward to interact with SageMaker services. The higher-level abstracted layer is designed for data scientists with limited AWS expertise, offering a simplified interface that hides complex infrastructure details.

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AWS Machine Learning Blog

JANUARY 15, 2025

Powered by generative AI services on AWS and large language models (LLMs) multi-modal capabilities, HCLTechs AutoWise Companion provides a seamless and impactful experience. Technical architecture The overall solution is implemented using AWS services and LangChain. AWS Glue AWS Glue is used for data cataloging.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

You can try out the models with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping provide data security.

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AWS Machine Learning Blog

NOVEMBER 6, 2024

For this post, we run the code in a Jupyter notebook within VS Code and use Python. Prerequisites Before you dive into the integration process, make sure you have the following prerequisites in place: AWS account – You’ll need an AWS account to access and use Amazon Bedrock. We walk through a Python example in this post.

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AWS Machine Learning Blog

DECEMBER 12, 2024

In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta Llama 3.1 The machine learning (ML) practitioners need to iterate over these settings before finally deploying the endpoint to SageMaker for inference.

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AWS Machine Learning Blog

FEBRUARY 6, 2025

Building upon a previous Machine Learning Blog post to create personalized avatars by fine-tuning and hosting the Stable Diffusion 2.1 We show how to then prepare the fine-tuned model to run on AWS Inferentia2 powered Amazon EC2 Inf2 instances , unlocking superior price performance for your inference workloads.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

This new cutting-edge image generation model, which was trained on Amazon SageMaker HyperPod , empowers AWS customers to generate high-quality images from text descriptions with unprecedented ease, flexibility, and creative potential. Large model is available today in the following AWS Regions: US East (N. By adding Stable Diffusion 3.5

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AWS Machine Learning Blog

DECEMBER 1, 2024

In this blog post, I will look at what makes physical AWS DeepRacer racing—a real car on a real track—different to racing in the virtual world—a model in a simulated 3D environment. The AWS DeepRacer League is wrapping up. The original AWS DeepRacer, without modifications, has a smaller speed range of about 2 meters per second.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

Amazon Titan Image Generator G1 v2 Exclusive to Amazon Bedrock, the Amazon Titan models incorporate the 25 years of experience that Amazon has innovating with AI and machine learning (ML) across its business. Solution overview This solution is running in AWS Region us-east-1. Anthropic Claude 3.5

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AWS Machine Learning Blog

OCTOBER 29, 2024

This solution uses decorators in your application code to capture and log metadata such as input prompts, output results, run time, and custom metadata, offering enhanced security, ease of use, flexibility, and integration with native AWS services. However, some components may incur additional usage-based costs.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

Developer tools The solution also uses the following developer tools: AWS Powertools for Lambda – This is a suite of utilities for Lambda functions that generates OpenAPI schemas from your Lambda function code. Python 3.9 or later Node.js The complete source code for this solution is available in the GitHub repository.

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