AWS Machine Learning Blog

SEPTEMBER 4, 2024

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. This entire workflow is shown in the following solution diagram.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

Operating such large-scale forecasting requires resilient, reusable, reproducible, and automated machine learning (ML) workflows with fast experimentation and continuous improvements. To improve forecasting accuracy, all involved ML models need to be retrained, and predictions need to be produced weekly, and in some cases daily.

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AWS Machine Learning Blog

APRIL 22, 2024

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. In this solution, HyperPod cluster instances use the LDAPS protocol to connect to the AWS Managed Microsoft AD via an NLB.

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. We use the Time Series Clustering using TSFresh + KMeans notebook, which is available on our GitHub repo.

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AWS Machine Learning Blog

JULY 31, 2024

Amazon Web Services (AWS) is committed to supporting the development of cutting-edge generative artificial intelligence (AI) technologies by companies and organizations across the globe. Let’s dive in and explore how these organizations are transforming what’s possible with generative AI on AWS.

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AWS Machine Learning Blog

MAY 31, 2023

Running machine learning (ML) workloads with containers is becoming a common practice. What you get is an ML development environment that is consistent and portable. With containers, scaling on a cluster becomes much easier. With containers, scaling on a cluster becomes much easier. Run the ML task on Amazon ECS.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

AWS offers a wide range of solutions for interacting with language models. Amazon SageMaker is a comprehensive, fully managed machine learning (ML) service to build, train, and deploy LLMs and other FMs at scale. Model deployment When deploying a fine-tuned model on AWS, multiple deployment strategies are available.

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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AWS Machine Learning Blog

APRIL 29, 2024

Close collaboration with AWS Trainium has also played a major role in making the Arcee platform extremely performant, not only accelerating model training but also reducing overall costs and enforcing compliance and data integrity in the secure AWS environment. Our cluster consisted of 16 nodes, each equipped with a trn1n.32xlarge

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AWS Machine Learning Blog

JUNE 11, 2024

In this post, we describe the scale of our AI offerings, the challenges with diverse AI workloads, and how we optimized mixed AI workload inference performance with AWS Graviton3 based c7g instances and achieved 20% throughput improvement, 30% latency reduction, and reduced our cost by 25–30%.

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

This allows SageMaker Studio users to perform petabyte-scale interactive data preparation, exploration, and machine learning (ML) directly within their familiar Studio notebooks, without the need to manage the underlying compute infrastructure. This same interface is also used for provisioning EMR clusters.

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AWS Machine Learning Blog

MAY 30, 2024

In this post, we introduce the Media Analysis and Policy Evaluation solution, which uses AWS AI and generative AI services to provide a framework to streamline video extraction and evaluation processes. This solution, powered by AWS AI and generative AI services, meets these needs.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. For this post we’ll use a provisioned Amazon Redshift cluster.

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AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

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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. Additionally, you can securely integrate and easily deploy your generative AI applications using the AWS tools you are already familiar with.

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AWS Machine Learning Blog

MAY 23, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of virtually infinite compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are rapidly adopting and using ML technologies to transform their businesses.

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Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It provides a large cluster of clusters on a single machine. AWS SageMaker also has a CLI for model creation and management.

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AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. M tokens/$) trained such models with AWS Trainium without losing any model quality. We’ll outline how we cost-effectively (3.2 billion in Pythia.

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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 data scientist is responsible for moving the code into SageMaker, either manually or by cloning it from a code repository such as AWS CodeCommit.

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AWS Machine Learning Blog

APRIL 7, 2023

As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy ML models closer to their end-customers to reduce latency and increase responsiveness of their applications. Even ground and aerial robotics can use ML to unlock safer, more autonomous operations.

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AWS Machine Learning Blog

MAY 31, 2024

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. Amazon SageMaker Amazon SageMaker is a fully managed ML service offered by AWS, designed to reduce the time and cost associated with training and tuning ML models at scale.

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AWS Machine Learning Blog

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. We also deep dive into the most common architectures and AWS resources to facilitate these integrations.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. Data is frequently kept in data lakes that can be managed by AWS Lake Formation , giving you the ability to implement fine-grained access control using a straightforward grant or revoke procedure.

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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 13, 2024

Prerequisites To implement the proposed solution, make sure that you have the following: An AWS account and a working knowledge of FMs, Amazon Bedrock , Amazon SageMaker , Amazon OpenSearch Service , Amazon S3 , and AWS Identity and Access Management (IAM). Amazon Titan Multimodal Embeddings model access in Amazon Bedrock.

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AWS Machine Learning Blog

NOVEMBER 22, 2024

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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IBM Journey to AI blog

JANUARY 16, 2024

Businesses are increasingly embracing data-intensive workloads, including high-performance computing, artificial intelligence (AI) and machine learning (ML). To aid in building more sustainable IT estates, IBM has partnered up with Amazon Web Services (AWS) to facilitate sustainable cloud modernization journeys.

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AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. We use the purpose-built geospatial container with SageMaker Processing jobs for a simplified, managed experience to create and run a cluster.

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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.

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AWS Machine Learning Blog

JULY 17, 2023

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. SageMaker HyperPod integrates the Slurm Workload Manager for cluster and training job orchestration.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

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