AWS Machine Learning Blog

NOVEMBER 22, 2023

When AWS launched purpose-built accelerators with the first release of AWS Inferentia in 2020, the M5 team quickly began to utilize them to more efficiently deploy production workloads , saving both cost and reducing latency. Like many ML organizations, accelerators are largely used to accelerate DL training and inference.

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AWS Machine Learning Blog

JANUARY 13, 2023

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. For Account ID , enter the AWS account ID of the owner of the accepter VPC.

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AWS Machine Learning Blog

DECEMBER 22, 2023

As a result, machine learning practitioners must spend weeks of preparation to scale their LLM workloads to large clusters of GPUs. Aligning SMP with open source PyTorch Since its launch in 2020, SMP has enabled high-performance, large-scale training on SageMaker compute instances. To mitigate this problem, SMP v2.0

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AWS Machine Learning Blog

APRIL 5, 2024

Among these models, the spatial fixed effect model yielded the highest mean R-squared value, particularly for the timeframe spanning 2014 to 2020. SageMaker Processing enables the flexible scaling of compute clusters to accommodate tasks of varying sizes, from processing a single city block to managing planetary-scale workloads.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN.

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AWS Machine Learning Blog

APRIL 19, 2024

We used AWS services including Amazon Bedrock , Amazon SageMaker , and Amazon OpenSearch Serverless in this solution. In this series, we use the slide deck Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023 to demonstrate the solution.

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AWS Machine Learning Blog

JUNE 7, 2023

In early 2020, research organizations across the world set the emphasis on model size, observing that accuracy correlated with number of parameters. For example, GPT-3 (2020) and BLOOM (2022) feature around 175 billion parameters, Gopher (2021) has 230 billion parameters, and MT-NLG (2021) 530 billion parameters.

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Dataconomy

APRIL 26, 2023

The strategic value of IoT development and data analytics Sierra Wireless Sierra Wireless , a wireless communications equipment designer and service provider, has been honing its focus on IoT software and managed services following its acquisition of M2M Group, a cluster of companies dedicated to IoT connectivity, in 2020.

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Flipboard

NOVEMBER 24, 2023

In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

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AWS Machine Learning Blog

MAY 25, 2023

in 2020 as a model where parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We provide an AWS Cloud Formation template to stand up all the resources required for building this solution.

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Hacker News

NOVEMBER 15, 2023

The Story of the Name Patrick Lewis, lead author of the 2020 paper that coined the term , apologized for the unflattering acronym that now describes a growing family of methods across hundreds of papers and dozens of commercial services he believes represent the future of generative AI.

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AWS Machine Learning Blog

OCTOBER 30, 2024

These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. For this demonstration, we use a public Amazon product dataset called Amazon Product Dataset 2020 from a kaggle competition.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

For a given frame, our features are inspired by the 2020 Big Data Bowl Kaggle Zoo solution ( Gordeev et al. ): we construct an image for each time step with the defensive players at the rows and offensive players at the columns. Prior to AWS, he obtained his MCS from West Virginia University and worked as computer vision researcher at Midea.

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AWS Machine Learning Blog

JULY 31, 2023

With SageMaker Processing, you can run a long-running job with a proper compute without setting up any compute cluster and storage and without needing to shut down the cluster. aws s3 cp {estimator_openfold.model_data} openfold_output/model.tar.gz !tar Shivam Patel is a Solutions Architect at AWS.

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The MLOps Blog

MARCH 23, 2023

Even for basic inference on LLM, multiple accelerators or multi-node computing clusters like multiple Kubernetes pods are required. But the issue we found was that MP is efficient in single-node clusters, but in a multi-node setting, the inference isn’t efficient. 2020 or Hoffman et al., For instance, a 1.5B

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AWS Machine Learning Blog

MAY 2, 2023

Managed Spot Training is supported in all AWS Regions where Amazon SageMaker is currently available. in 2020 as a model where parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. RAG models were introduced by Lewis et al.

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AWS Machine Learning Blog

APRIL 3, 2023

A myriad of instruction tuning research has been performed since 2020, producing a collection of various tasks, templates, and methods. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering.

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AWS Machine Learning Blog

APRIL 18, 2023

c/o Ernst & Young LLPSeattle, Washington Attention: Corporate Secretary (2) For the purpose of Article III of the Securities Exchange Act of 1934, the registrant’s name and address are as follows:(3) The registrant’s Exchange Act reportable time period is from and includingJanuary 1, 2020 to the present.(4)

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DagsHub

JULY 15, 2024

T5 : T5 stands for Text-to-Text Transfer Transformer, developed by Google in 2020. Whether you are opting to fine-tune on a local machine or the cloud, predominant factors related to cost will be fine-tuning time, GPU clusters, and storage. You can automatically manage and monitor your clusters using AWS, GCD, or Azure.

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AWS Machine Learning Blog

APRIL 18, 2023

c/o Ernst & Young LLPSeattle, Washington Attention: Corporate Secretary (2) For the purpose of Article III of the Securities Exchange Act of 1934, the registrant’s name and address are as follows:(3) The registrant’s Exchange Act reportable time period is from and includingJanuary 1, 2020 to the present.(4)

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AWS Machine Learning Blog

AUGUST 14, 2023

Answer: 2021 ### Context: NLP Cloud developed their API by mid-2020 and they added many pre-trained open-source models since then. These attributes are only default values; you can override them and retain granular control over the AWS models you create. [Name]: Fred [Position]: Co-founder and CEO [Company]: Platform.sh

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AWS Machine Learning Blog

OCTOBER 11, 2024

Amazon Bedrock Knowledge Bases provides industry-leading embeddings models to enable use cases such as semantic search, RAG, classification, and clustering, to name a few, and provides multilingual support as well. You can set up the notebook in any AWS Region where Amazon Bedrock Knowledge Bases is available.

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