AWS Machine Learning Blog

JUNE 23, 2023

Additionally, you can use AWS Lambda directly to expose your models and deploy your ML applications using your preferred open-source framework, which can prove to be more flexible and cost-effective. We also show you how to automate the deployment using the AWS Cloud Development Kit (AWS CDK). Now, let’s set up the environment.

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AWS Machine Learning Blog

MAY 31, 2024

Genomic language models are a new and exciting field in the application of large language models to challenges in genomics. In this blog post and open source project , we show you how you can pre-train a genomics language model, HyenaDNA , using your genomic data in the AWS Cloud.

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AWS Machine Learning Blog

SEPTEMBER 13, 2023

Amazon Comprehend is a fully, managed service that uses natural language processing (NLP) to extract insights about the content of documents. In this post, we use Amazon Comprehend and other AWS services to analyze and extract new insights from a repository of documents. In this example, we use text formatted files.

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AWS Machine Learning Blog

JUNE 12, 2024

With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the AWS tools without having to manage any infrastructure. To implement this architecture, we take advantage of AWS Step Functions to build the overall workflow.

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AWS Machine Learning Blog

MAY 4, 2023

AWS has been innovating with purpose-built chips to address the growing need for powerful, efficient, and cost-effective compute hardware. You can use ml.trn1 and ml.inf2 compatible AWS Deep Learning Containers (DLCs) for PyTorch, TensorFlow, Hugging Face, and large model inference (LMI) to easily get started. petaflops for BF16/FP16.

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AWS Machine Learning Blog

AUGUST 30, 2023

The proposed solution in this post uses fine-tuning of pre-trained large language models (LLMs) to help generate summarizations based on findings in radiology reports. This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services.

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AWS Machine Learning Blog

APRIL 7, 2023

Retailers can deliver more frictionless experiences on the go with natural language processing (NLP), real-time recommendation systems, and fraud detection. 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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Flipboard

NOVEMBER 20, 2023

We use two AWS Media & Entertainment Blog posts as the sample external data, which we convert into embeddings with the BAAI/bge-small-en-v1.5 Prerequisites To follow the steps in this post, you need to have an AWS account and an AWS Identity and Access Management (IAM) role with permissions to create and access the solution resources.

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AWS Machine Learning Blog

MAY 22, 2024

Using machine learning (ML) and natural language processing (NLP) to automate product description generation has the potential to save manual effort and transform the way ecommerce platforms operate. For details, see Creating an AWS account. For more information, see Configure the AWS CLI.

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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

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. release, AWS customers can now do same things as they could with PyTorch 1.x 24xlarge with AWS PyTorch 2.0

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AWS Machine Learning Blog

MAY 31, 2023

You can submit a document from the AWS Management Console , AWS Command Line Interface (AWS CLI), or AWS SDK and receive the translated document in real time while maintaining the format of the original document. The translated file is automatically saved to your browser’s downloaded folder, usually to Downloads.

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AWS Machine Learning Blog

APRIL 17, 2024

With AWS, you can deploy this solution in a serverless, scalable, and fully event-driven architecture. This post demonstrates a proof of concept built on two key AWS services well suited for graph knowledge representation and natural language processing: Amazon Neptune and Amazon Bedrock.

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AWS Machine Learning Blog

MARCH 25, 2024

Amazon Transcribe is an AWS service that allows customers to convert speech to text in either batch or streaming mode. It uses machine learning–powered automatic speech recognition (ASR), automatic language identification, and post-processing technologies. For more information about data privacy, see the Data Privacy FAQ.

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Flipboard

FEBRUARY 15, 2023

SageMaker projects are provisioned using AWS Service Catalog products. Prerequisites To implement this solution, you must have an AWS Identity and Access Management (IAM) role that allows connection to SageMaker and Amazon S3. He works with Machine Learning Startups to build and deploy AI/ML applications on AWS.

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AWS Machine Learning Blog

APRIL 16, 2024

IAM role – SageMaker requires an AWS Identity and Access Management (IAM) role to be assigned to a SageMaker Studio domain or user profile to manage permissions effectively. Create database connections The built-in SQL browsing and execution capabilities of SageMaker Studio are enhanced by AWS Glue connections. or later image versions.

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AWS Machine Learning Blog

DECEMBER 14, 2023

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart , a machine learning (ML) hub offering models, algorithms, and solutions. This technique is particularly useful for knowledge-intensive natural language processing (NLP) tasks.

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AWS Machine Learning Blog

NOVEMBER 21, 2024

The integrated approach and ease of use of Amazon Bedrock in deploying large language models (LLMs), along with built-in features that facilitate seamless integration with other AWS services like Amazon Kendra, made it the preferred choice. By using Claude 3’s vision capabilities, we could upload image-rich PDF documents.

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AWS Machine Learning Blog

APRIL 25, 2024

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. Solution overview Amazon Transcribe is the go-to service for speaker diarization in AWS. Make sure the AWS account has a service quota for hosting a SageMaker endpoint for an ml.g5.2xlarge instance.

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AWS Machine Learning Blog

OCTOBER 18, 2024

We demonstrate this solution by walking you through a comprehensive step-by-step guide on how to fine-tune YOLOv8 , a real-time object detection model, on Amazon Web Services (AWS) using a custom dataset. The process uses a single ml.g5.2xlarge instance (providing one NVIDIA A10G Tensor Core GPU) with SageMaker for fine-tuning.

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AWS Machine Learning Blog

APRIL 1, 2024

Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. The following figure shows how FSDP works for two data parallel processes. In the following sections, we explain the end-to-end process in more detail.

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AWS Machine Learning Blog

APRIL 19, 2024

We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) We used AWS services including Amazon Bedrock , Amazon SageMaker , and Amazon OpenSearch Serverless in this solution. In this post, we demonstrate a different approach. The models are enabled for use immediately.

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AWS Machine Learning Blog

OCTOBER 30, 2024

Global Resiliency is a new Amazon Lex capability that enables near real-time replication of your Amazon Lex V2 bots in a second AWS Region. We showcase the replication process of bot versions and aliases across multiple Regions. Solution overview For this exercise, we create a BookHotel bot as our sample bot.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

git clone --depth 2 --filter=blob:none --no-checkout [link] && cd amazon-bedrock-samples && git checkout main rag-solutions/contextual-chatbot-using-knowledgebase Upload your knowledge dataset to Amazon S3 We download the dataset for our knowledge base and upload it into a S3 bucket. Note this is one single git clone command.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL. We use Anthropic Claude v2.1

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AWS Machine Learning Blog

SEPTEMBER 7, 2023

Measurement data is produced at the edge by a piece of industrial equipment (here simulated by an AWS Lambda function). Amazon S3 is a durable, performant, and low-cost storage solution that allows you to serve large volumes of data to a machine learning training process. Evaluate the performance of the model in production.

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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

OCTOBER 6, 2023

Background of multimodality models Machine learning (ML) models have achieved significant advancements in fields like natural language processing (NLP) and computer vision, where models can exhibit human-like performance in analyzing and generating content from a single source of data. model, which is more than 15 GB in size.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

It provides a common framework for assessing the performance of natural language processing (NLP)-based retrieval models, making it straightforward to compare different approaches. You may be prompted to subscribe to this model through AWS Marketplace. On the AWS Marketplace listing , choose Continue to subscribe.

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AWS Machine Learning Blog

JULY 24, 2024

Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. In this example, we download the data from a Hugging Face dataset. For instructions to generate a token, see User access tokens.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

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. For more information, see Overview of access management: Permissions and policies.

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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. to the local directory as tar.gz

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AWS Machine Learning Blog

MAY 13, 2024

In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. Since then, Amazon Web Services (AWS) has introduced new services such as Amazon Bedrock. It is time-consuming but, at the same time, critical.

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AWS Machine Learning Blog

JUNE 13, 2023

To further complicate things, although smaller models such as Falcon-7B can generally fit on a single GPU such as an NVIDIA A10G instance that powers AWS G5 instance types, larger models like Falcon-40B cannot. Note that the actual model has not been downloaded or packaged into this file. amazonaws.com/djl-inference:0.22.1-deepspeed0.8.3-cu118"

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AWS Machine Learning Blog

OCTOBER 19, 2023

Amazon Comprehend is a natural language processing (NLP) service that uses ML to uncover insights and relationships in unstructured data, with no managing infrastructure or ML experience required. Download the SageMaker Data Wrangler flow. Prerequisites For this walkthrough, you should have the following: An AWS account.

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AWS Machine Learning Blog

AUGUST 7, 2024

Prerequisites To clone the GitHub repository to your local machine, open a terminal window and run the following commands: git clone [link] cd knowledge-bases/features-examples/03-advanced-concepts/reranking Prepare the dataset Download the book from the Project Gutenberg website. Pallavi Nargund is a Principal Solutions Architect at AWS.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

This file contains the required configurations for the Deep Java Library (DJL) model server to download and host the model. The task parameter is used to define the natural language processing (NLP) task. The model_loading_timeout sets the timeout value for downloading and loading the model to serve inference.

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