AWS Machine Learning Blog

OCTOBER 5, 2023

In this post, we walk through how to fine-tune Llama 2 on AWS Trainium , a purpose-built accelerator for LLM training, to reduce training times and costs. We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw.

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JUNE 20, 2023

For reference, GPT-3, an earlier generation LLM has 175 billion parameters and requires months of non-stop training on a cluster of thousands of accelerated processors. The Carbontracker study estimates that training GPT-3 from scratch may emit up to 85 metric tons of CO2 equivalent, using clusters of specialized hardware accelerators.

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AWS Machine Learning Blog

JANUARY 24, 2024

We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace. Cohere Embed comes in two forms, an English language model and a multilingual model, both of which are now available on Amazon Bedrock.

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AWS Machine Learning Blog

APRIL 29, 2024

Historically, natural language processing (NLP) would be a primary research and development expense. In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows.

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AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture and is intended for commercial and research use in English. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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NOVEMBER 17, 2023

Set up a MongoDB cluster To create a free tier MongoDB Atlas cluster, follow the instructions in Create a Cluster. Specify the AWS Lambda function that will interact with MongoDB Atlas and the LLM to provide responses. Delete the MongoDB Atlas cluster. As always, AWS welcomes feedback.

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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. Choose Manage.

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AWS Machine Learning Blog

JANUARY 17, 2024

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. In this post, we demonstrate how to deploy and fine-tune Llama 2 on Trainium and AWS Inferentia instances in SageMaker JumpStart.

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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

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.

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

Cost optimization – The serverless nature of the integration means you only pay for the compute resources you use, rather than having to provision and maintain a persistent cluster. This same interface is also used for provisioning EMR clusters. This same interface is also used for provisioning EMR clusters.

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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. Distributed model training requires a cluster of worker nodes that can scale. The following figure shows how FSDP works for two data parallel processes.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

Key AWS services used include: Amazon Bedrock Including Anthropics Claude 3.5 Sonnet model for natural language processing. This means artists can focus on their creative process rather than worrying about precise phrasing or navigating complex menu structures.

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AWS Machine Learning Blog

MARCH 11, 2025

The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing queries to the most relevant expert clusters. By integrating this model with Amazon SageMaker AI , you can benefit from the AWS scalable infrastructure while maintaining high-quality language model capabilities.

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AWS Machine Learning Blog

FEBRUARY 2, 2024

Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with language in a numeric form. Then we use K-Means to identify a set of cluster centers. A visual representation of the silhouette score can be seen in the following figure.

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AWS Machine Learning Blog

DECEMBER 29, 2022

AWS offers tools such as RStudio on SageMaker and Amazon Redshift to help tackle these challenges. Note: If you already have an RStudio domain and Amazon Redshift cluster you can skip this step. Amazon Redshift Serverless cluster. I acknowledge that AWS CloudFormation might create IAM resources with custom names.

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AWS Machine Learning Blog

JANUARY 31, 2024

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). Text embedding refers to the process of transforming text into numerical representations that reside in a high-dimensional vector space. You can use it via either the Amazon Bedrock REST API or the AWS SDK.

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AWS Machine Learning Blog

AUGUST 8, 2024

Webex’s focus on delivering inclusive collaboration experiences fuels their innovation, which uses artificial intelligence (AI) and machine learning (ML), to remove the barriers of geography, language, personality, and familiarity with technology. Webex works with the world’s leading business and productivity apps—including AWS.

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AWS Machine Learning Blog

APRIL 17, 2023

Using RStudio on SageMaker and Amazon EMR together, you can continue to use the RStudio IDE for analysis and development, while using Amazon EMR managed clusters for larger data processing. In this post, we demonstrate how you can connect your RStudio on SageMaker domain with an EMR cluster. Choose Create stack.

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AWS Machine Learning Blog

JUNE 21, 2024

To accomplish this, eSentire built AI Investigator, a natural language query tool for their customers to access security platform data by using AWS generative artificial intelligence (AI) capabilities. The additional benefit of SageMaker notebook instances is its streamlined integration with eSentire’s AWS environment.

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AWS Machine Learning Blog

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. In the past few years, numerous customers have been using the AWS Cloud for LLM training. We recommend working with your AWS account team or contacting AWS Sales to determine the appropriate Region for your LLM workload.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise. Clusters are provisioned with the instance type and count of your choice and can be retained across workloads. As a result of this flexibility, you can adapt to various scenarios.

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MARCH 9, 2023

In this post, we review the technical requirements and application design considerations for fine-tuning and serving hyper-personalized AI models at scale on AWS. For example, the G5 instance type, which is equipped with NVIDIA A10g GPUs (unique to AWS), offers a strong price-performance ratio, both for model training and hosting.

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AWS Machine Learning Blog

MARCH 2, 2023

In our solution, we implement a hyperparameter grid search on an EKS cluster for tuning a bert-base-cased model for classifying positive or negative sentiment for stock market data headlines. A desired cluster can simply be configured using the eks.conf file and launched by running the eks-create.sh to launch the cluster.

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AWS Machine Learning Blog

MARCH 4, 2024

However, when employing the use of traditional natural language processing (NLP) models, they found that these solutions struggled to fully understand the nuanced feedback found in open-ended survey responses. About the authors Kinman Lam is an ISV/DNB Solution Architect for AWS.

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AWS Machine Learning Blog

SEPTEMBER 16, 2024

The clustered regularly interspaced short palindromic repeat (CRISPR) technology holds the promise to revolutionize gene editing technologies, which is transformative to the way we understand and treat diseases. We also provided code that can help you jumpstart your biology applications in AWS.

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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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DECEMBER 6, 2023

Tools like LangChain , combined with a large language model (LLM) powered by Amazon Bedrock or Amazon SageMaker JumpStart , simplify the implementation process. Click here to open the AWS console and follow along. The model then uses a clustering algorithm to group the sentences into clusters.

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AWS Machine Learning Blog

APRIL 24, 2024

With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. It is hosted on Amazon Elastic Container Service (Amazon ECS) with AWS Fargate , and it is accessed using an Application Load Balancer. It serves as the data source to the knowledge base.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

Prerequisites To try out this solution using SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all of your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker. Appendix Language models such as Meta Llama are more than 10 GB or even 100 GB in size.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

One of the several challenges faced was adapting the existing on-premises pipeline solution for use on AWS. The solution involved two key components: Modifying and extending existing code – The first part of our solution involved the modification and extension of our existing code to make it compatible with AWS infrastructure.

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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

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

SEPTEMBER 11, 2024

PBAs, such as graphics processing units (GPUs), have an important role to play in both these phases. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. Suppliers of data center GPUs include NVIDIA, AMD, Intel, and others.

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AWS Machine Learning Blog

JUNE 6, 2024

This makes the models especially powerful at tasks such as clustering for long documents like legal text or product documentation. Support for bilingual text input – Recent research shows that multilingual models without specific language training show strong biases towards English grammatical structures in embeddings. Choose Deploy.

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AWS Machine Learning Blog

MAY 3, 2023

Furthermore, the dynamic nature of a customer’s data can also result in a large variance of the processing time and resources required to optimally complete the feature engineering. AWS customer Vericast is a marketing solutions company that makes data-driven decisions to boost marketing ROIs for its clients.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

In this post, Reveal experts showcase how they used Amazon Comprehend in their document processing pipeline to detect and redact individual pieces of PII. Amazon Comprehend is a fully managed and continuously trained natural language processing (NLP) service that can extract insight about the content of a document or text.

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