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

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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Applied Data Science

AUGUST 2, 2021

Download the free, unabridged version here. They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Give this technique a try to take your team’s ML modelling to the next level.

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Towards AI

JUNE 27, 2023

Let’s get started with the best machine learning (ML) developer tools: TensorFlow TensorFlow, developed by the Google Brain team, is one of the most utilized machine learning tools in the industry. This open-source library is renowned for its capabilities in numerical computation, particularly in large-scale machine learning projects.

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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

MAY 1, 2024

Using the Neuron Distributed library with SageMaker SageMaker is a fully managed service that provides developers, data scientists, and practitioners the ability to build, train, and deploy machine learning (ML) models at scale. Cluster update is currently enabled for the TRN1 instance family as well as P and G GPU-based instance types.

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. Second, open source Metaflow provides the necessary software infrastructure to build production-grade ML/AI systems in a developer-friendly manner.

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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. Solution overview You can use DeepSeeks distilled models within the AWS managed machine learning (ML) infrastructure. Pranav Murthy is an AI/ML Specialist Solutions Architect at AWS.

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Flipboard

FEBRUARY 16, 2023

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. As part of a single cluster run, you can spin up a cluster of Trn1 instances with Trainium accelerators. Trn1 UltraClusters can host up to 30,000 Trainium devices and deliver up to 6 exaflops of compute in a single cluster.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

jpg", "prompt": "Which part of Virginia is this letter sent from", "completion": "Richmond"} SageMaker JumpStart SageMaker JumpStart is a powerful feature within the SageMaker machine learning (ML) environment that provides ML practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs).

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AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage. When an On-Demand job is launched, it goes through five phases: Starting, Downloading, Training, Uploading, and Completed.

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AWS Machine Learning Blog

MAY 8, 2023

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization. About the Authors Melanie Li is a Senior AI/ML Specialist TAM at AWS based in Sydney, Australia.

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AWS Machine Learning Blog

DECEMBER 15, 2023

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas , allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. Enter a connection name such as demo and choose your desired Amazon DocumentDB cluster.

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AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. We recently developed four more new models.

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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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AWS Machine Learning Blog

JUNE 11, 2024

In these cases, the model sizes are smaller, which means the communication overhead with GPUs or ML accelerator instances outweighs their compute performance benefits. As early adopters of Graviton for ML workloads, it was initially challenging to identify the right software versions and the runtime tunings.

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AWS Machine Learning Blog

MAY 23, 2024

By distributing experts across workers, expert parallelism addresses the high memory requirements of loading all experts on a single device and enables MoE training on a larger cluster. The following figure offers a simplified look at how expert parallelism works on a multi-GPU cluster.

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AWS Machine Learning Blog

FEBRUARY 1, 2023

In late 2022, AWS announced the general availability of Amazon EC2 Trn1 instances powered by AWS Trainium —a purpose-built machine learning (ML) accelerator optimized to provide a high-performance, cost-effective, and massively scalable platform for training deep learning models in the cloud.

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AWS Machine Learning Blog

APRIL 29, 2024

Trainium is the second-generation machine learning (ML) accelerator that AWS purpose built to help developers access high-performance model training accelerators to help lower training costs by up to 50% over comparable Amazon Elastic Compute Cloud (Amazon EC2) instances. Our cluster consisted of 16 nodes, each equipped with a trn1n.32xlarge

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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

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. SageMaker Training is a managed batch ML compute service that reduces the time and cost to train and tune models at scale without the need to manage infrastructure. SageMaker-managed clusters of ml.p4d.24xlarge

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AWS Machine Learning Blog

JUNE 7, 2023

Training setup We provisioned a managed compute cluster comprised of 16 dl1.24xlarge instances using AWS Batch. We developed an AWS Batch workshop that illustrates the steps to set up the distributed training cluster with AWS Batch. The distributed training workshop illustrates the steps to set up the distributed training cluster.

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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. Hugging Face is a popular open source hub for machine learning (ML) models. You use the same script for downloading the model file when creating the SageMaker endpoint.

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AWS Machine Learning Blog

MAY 31, 2024

Here, we use AWS HealthOmics storage as a convenient and cost-effective omic data store and Amazon Sagemaker as a fully managed machine learning (ML) service to train and deploy the model. With SageMaker Training, a managed batch ML compute service, users can efficiently train models without having to manage the underlying infrastructure.

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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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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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O'Reilly Media

SEPTEMBER 19, 2023

A basic, production-ready cluster priced out to the low-six-figures. A company then needed to train up their ops team to manage the cluster, and their analysts to express their ideas in MapReduce. Plus there was all of the infrastructure to push data into the cluster in the first place. Goodbye, Hadoop. And it was good.

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). Typical unsupervised learning tasks include clustering (e.g., It’s a fascinating field that can seem daunting at first, but I assure you, with the right mindset and resources, anyone can master it.

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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. Training steps To run the training, we use SLURM managed multi-node Amazon Elastic Compute Cloud ( Amazon EC2 ) Trn1 cluster, with each node containing a trn1.32xl instance.

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AWS Machine Learning Blog

OCTOBER 16, 2023

As an AI-powered solution, Veriff needs to create and run dozens of machine learning (ML) models in a cost-effective way. Infrastructure and development challenges Veriff’s backend architecture is based on a microservices pattern, with services running on different Kubernetes clusters hosted on AWS infrastructure.

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AWS Machine Learning Blog

JUNE 7, 2023

The model weights are available to download, inspect and deploy anywhere. Starting June 7th, both Falcon LLMs will also be available in Amazon SageMaker JumpStart, SageMaker’s machine learning (ML) hub that offers pre-trained models, built-in algorithms, and pre-built solution templates to help you quickly get started with ML.

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AWS Machine Learning Blog

JANUARY 24, 2024

Text representation with Embed – Developers can access endpoints that capture the semantic meaning of text, enabling applications such as vector search engines, text classification and clustering, and more. Next, you set up a Weaviate cluster. Subscribe to the Weaviate Kubernetes Cluster on AWS Marketplace.

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AWS Machine Learning Blog

APRIL 19, 2024

This solution includes the following components: Amazon Titan Text Embeddings is a text embeddings model that converts natural language text, including single words, phrases, or even large documents, into numerical representations that can be used to power use cases such as search, personalization, and clustering based on semantic similarity.

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Mlearning.ai

MAY 19, 2023

Here is HuggingFace Link: [link] From the Mosaic ML paper. Here is the HuggingFace Link: [link] From the Mosaic ML paper. This approach offers several benefits, including the elimination of the need to download the entire dataset before commencing training. This model was trained with 9.6M This model was trained with 86M tokens.

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AWS Machine Learning Blog

JANUARY 6, 2023

We downloaded the data from AWS Data Exchange and processed it in AWS Glue to generate KG files. In Part 2 , we demonstrated how to use Amazon Neptune ML (in Amazon SageMaker ) to train the KG and create KG embeddings. The KG files were stored in Amazon Simple Storage Service (Amazon S3) and then loaded in Amazon Neptune.

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AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them.

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AWS Machine Learning Blog

JULY 31, 2023

Machine learning (ML) methods can help identify suitable compounds at each stage in the drug discovery process, resulting in more streamlined drug prioritization and testing, saving billions in drug development costs (for more information, refer to AI in biopharma research: A time to focus and scale ). that runs run_alphafold.py

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