AWS Machine Learning Blog

JANUARY 29, 2025

In this post, we explore how to deploy distilled versions of DeepSeek-R1 with Amazon Bedrock Custom Model Import, making them accessible to organizations looking to use state-of-the-art AI capabilities within the secure and scalable AWS infrastructure at an effective cost. Watch this video demo for a step-by-step guide.

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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

MAY 30, 2024

Because Amazon Bedrock is serverless, you don’t have to manage infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. AWS Prototyping developed an AWS Cloud Development Kit (AWS CDK) stack for deployment following AWS best practices.

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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

MAY 23, 2023

You can also access JumpStart models using the SageMaker Python SDK. In April 2023, AWS unveiled Amazon Bedrock , which provides a way to build generative AI-powered apps via pre-trained models from startups including AI21 Labs , Anthropic , and Stability AI. Clone and set up the AWS CDK application.

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AWS Machine Learning Blog

OCTOBER 10, 2023

Customers often need to train a model with data from different regions, organizations, or AWS accounts. Existing partner open-source FL solutions on AWS include FedML and NVIDIA FLARE. These open-source packages are deployed in the cloud by running in virtual machines, without using the cloud-native services available on AWS.

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AWS Machine Learning Blog

JULY 26, 2023

Generative AI Foundations on AWS is a new technical deep dive course that gives you the conceptual fundamentals, practical advice, and hands-on guidance to pre-train, fine-tune, and deploy state-of-the-art foundation models on AWS and beyond. Feel free to reach out to me on Medium, LinkedIn , GitHub , or through your AWS teams.

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AWS Machine Learning Blog

AUGUST 30, 2023

This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. In the following sections, we demonstrate fine-tuning an LLM available on SageMaker JumpStart for summarization of a domain-specific task via the SageMaker Python SDK. We use an ml.t3.medium

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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. The demo code is available in the GitHub repository.

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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, 2023

To reduce the barrier to entry of ML at the edge, we wanted to demonstrate an example of deploying a pre-trained model from Amazon SageMaker to AWS Wavelength , all in less than 100 lines of code. In this post, we demonstrate how to deploy a SageMaker model to AWS Wavelength to reduce model inference latency for 5G network-based applications.

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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. Alternatively, you can also use AWS Systems Manager and run a command such as the following to start the session.

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Flipboard

DECEMBER 11, 2024

Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. Youre redirected to the AWS CloudFormation console to deploy a stack to configure VPC resources.

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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

NOVEMBER 7, 2023

Today, we are excited to unveil three generative AI demos, licensed under MIT-0 license : Amazon Kendra with foundational LLM – Utilizes the deep search capabilities of Amazon Kendra combined with the expansive knowledge of LLMs. Having the right setup in place is the first step towards a seamless deployment of the demos. Python 3.6

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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

OCTOBER 21, 2024

However, customers who want to deploy LLMs in their own self-managed workflows for greater control and flexibility of underlying resources can use these LLMs optimized on top of AWS Inferentia2-powered Amazon Elastic Compute Cloud (Amazon EC2) Inf2 instances. Main components The following are the main components of the solution.

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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

JUNE 11, 2024

Such a flow can run in each needed AWS Region supported by Amazon Bedrock to address any compliance needs of their customers. In addition, to secure the usage of Amazon Bedrock with least privilege, Wiz uses AWS permission sets and follows AWS best practices. Learn more about Wiz and check out a live demo.

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AWS Machine Learning Blog

AUGUST 1, 2023

With the power of state-of-the-art techniques, the creative agency can support their customer by using generative AI models within their secure AWS environment. AWS has also developed hardware and chips using AWS Inferentia2 for high performance at the lowest cost for generative AI inference.

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AWS Machine Learning Blog

APRIL 11, 2023

With Streamlit , developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. As an example, we use a custom Amazon Rekognition demo, which will annotate and label an uploaded image. Create Studio using JupyterLab 3.0

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AWS Machine Learning Blog

NOVEMBER 22, 2023

The task involved writing Python code to read data, transform it, and then visualize it in an interesting map. Prerequisites There are a few prerequisites to deploy the demo. You’ll need access to an AWS account with an access key or AWS Identity and Access Management (IAM) role with permissions to Amazon Bedrock and Amazon Location.

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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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The MLOps Blog

MAY 10, 2023

How to save a trained model in Python? Saving trained model with pickle The pickle module can be used to serialize and deserialize the Python objects. For saving the ML models used as a pickle file, you need to use the Pickle module that already comes with the default Python installation. Now let’s see how we can save our model.

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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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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. Make sure to choose the medical-image-ai Python kernel when running the TCIA notebooks in Studio Lab.

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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

SEPTEMBER 25, 2024

Built on AWS technologies like AWS Lambda , Amazon API Gateway , and Amazon DynamoDB , this tool automates the creation of customizable templates and supports both text and image inputs. The API communicates with a Python-based Lambda function to process requests. The following diagram illustrates the solution architecture.

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AWS Machine Learning Blog

JANUARY 29, 2025

In this post, we explore how to deploy distilled versions of DeepSeek-R1 with Amazon Bedrock Custom Model Import, making them accessible to organizations looking to use state-of-the-art AI capabilities within the secure and scalable AWS infrastructure at an effective cost. Watch this video demo for a step-by-step guide.

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AWS Machine Learning Blog

FEBRUARY 14, 2023

The Amazon Lex chatbot can be integrated into Amazon Kendra using a direct integration or via an AWS Lambda function. The use of the AWS Lambda function will provide you with fine-grained control of the Amazon Kendra API calls. For instructions on creating S3 buckets, please refer to AWS Documentation – Creating a bucket.

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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. If omitted, this step will be skipped.

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AWS Machine Learning Blog

JULY 2, 2024

For production use, it is recommended to use a more robust frontend framework such as AWS Amplify , which provides a comprehensive set of tools and services for building scalable and secure web applications. The process is straightforward, thanks to the user-friendly interface and step-by-step guidance provided by the AWS Management Console.

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AWS Machine Learning Blog

FEBRUARY 8, 2024

The following demo recording highlights Agents and Knowledge Bases for Amazon Bedrock functionality and technical implementation details. Each action group can specify one or more API paths, whose business logic is run through the AWS Lambda function associated with the action group. Postprocessing is disabled by default.

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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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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

FEBRUARY 19, 2024

Multiple device and accelerator support – DJL Serving supports deploying models on CPU, GPU, and AWS Inferentia. This instructs DJLServing to use the Python engine. engine=Python max_idle_time=600 Next, we create our model.py example in this demo can be seen in the GitHub repo. The serving.properties is shown as follows.

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AWS Machine Learning Blog

OCTOBER 2, 2023

The models show state-of-the-art performance in Python, C++, Java, PHP, C#, TypeScript, and Bash, and have the potential to save developers’ time and make software workflows more efficient. The model is deployed in an AWS secure environment and under your VPC controls, helping ensure data security.

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