Flipboard

FEBRUARY 2, 2023

There are around 3,000 and 4,000 plays from four NFL seasons (2018–2021) for punt and kickoff plays, respectively. Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season. He works with AWS customers to solve business problems with artificial intelligence and machine learning.

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Cross Validation ML ML Machine Learning 123

AWS Machine Learning Blog

SEPTEMBER 11, 2024

In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. It also means not all workloads are equally suitable for acceleration.

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AWS ML ML Clustering 92

AWS Machine Learning Blog

APRIL 3, 2023

Prerequisites You need an AWS account to use this solution. To run this JumpStart 1P Solution and have the infrastructure deployed to your AWS account, you need to create an active Amazon SageMaker Studio instance (refer to Onboard to Amazon SageMaker Domain ).

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Database AWS Machine Learning Machine Learning 93

AWS Machine Learning Blog

NOVEMBER 9, 2023

This retrieval can happen using different algorithms. In these two studies, commissioned by AWS, developers were asked to create a medical software application in Java that required use of their internal libraries. Xiaofei Ma is an Applied Science Manager in AWS AI Labs.

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AWS Machine Learning Blog

MAY 2, 2023

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). Managed Spot Training is supported in all AWS Regions where Amazon SageMaker is currently available.

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Algorithm Machine Learning Machine Learning Natural Language Processing 86

AWS Machine Learning Blog

OCTOBER 2, 2024

Laying the groundwork: Collecting ground truth data The foundation of any successful agent is high-quality ground truth data—the accurate, real-world observations used as reference for benchmarks and evaluating the performance of a model, algorithm, or system. Additionally, check out the service introduction video from AWS re:Invent 2023.

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AI AI AWS Machine Learning 93

AWS Machine Learning Blog

AUGUST 16, 2023

Our solution is based on the DINO algorithm and uses the SageMaker distributed data parallel library (SMDDP) to split the data over multiple GPU instances. The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018. tif" --include "_B03.tif" tif" --include "_B04.tif"

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ML ML Data Scientist AWS 81

AWS Machine Learning Blog

APRIL 6, 2023

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We also demonstrate the performance of our state-of-the-art point cloud-based product lifecycle prediction algorithm.

Machine Learning 93

Machine Learning Machine Learning ML ML 93

Mlearning.ai

JANUARY 26, 2023

BUILDING EARTH OBSERVATION DATA CUBES ON AWS. 2018, July). In IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium (pp. AWS , GCP , Azure , CreoDIAS , for example, are not open-source, nor are they “standard”. Big ones can: AWS is benefiting a lot from these concepts. Data, 4(3), 92.

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AWS Database Data Science Clean Data 98

AWS Machine Learning Blog

JULY 13, 2023

Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers. Train a binary classification model using the SageMaker built-in XGBoost algorithm. PMLR, 2018. [2] 3] Dai, Wei, et al.

Clustering 76

Clustering Algorithm Deep Learning Deep Learning 76

AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. Each season consists of around 17,000 plays. probability.

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ML ML Machine Learning Machine Learning 75

AWS Machine Learning Blog

MAY 22, 2023

Prerequisites To get started, all you need is an AWS account in which you can use Studio. About the authors Laurent Callot is a Principal Applied Scientist and manager at AWS AI Labs who has worked on a variety of machine learning problems, from foundational models and generative AI to forecasting, anomaly detection, causality, and AI Ops.

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AWS Machine Learning Machine Learning Natural Language Processing 105

AWS Machine Learning Blog

FEBRUARY 5, 2024

Spatial data, which relates to the physical position and shape of objects, often contains complex patterns and relationships that may be difficult for traditional algorithms to analyze. Janosch Woschitz is a Senior Solutions Architect at AWS, specializing in geospatial AI/ML. Emmett joined AWS in 2020 and is based in Austin, TX.

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ML ML AWS Analytics 100

AWS Machine Learning Blog

MAY 5, 2023

This is a joint post co-written by AWS and Voxel51. To learn more about Ground Truth, refer to Label Data , Amazon SageMaker Data Labeling FAQs , and the AWS Machine Learning Blog. Voxel51 is the company behind FiftyOne, the open-source toolkit for building high-quality datasets and computer vision models. Join the FiftyOne community!

Machine Learning 87

Machine Learning Machine Learning AWS ML 87

Explosion

AUGUST 1, 2019

Based on the (fairly vague) marketing copy, AWS might be doing something similar in SageMaker. 2018) in using the vector for the class token to represent the sentence, and passing this vector forward into a softmax layer in order to perform classification. in accuracy depending on the task and dataset. and follows Devlin et al.

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Natural Language Processing Machine Learning Machine Learning AWS 52

The MLOps Blog

JULY 18, 2023

Then we subsequently try to run audio fingerprinting type algorithms on top of it so that we can actually identify specifically who those people are if we’ve seen them in the past. We need to do that, but we don’t really know what those topics are, so we use some algorithms. We call it our “format stage.”

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AI AI Natural Language Processing Machine Learning 52

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 Python Machine Learning Machine Learning 121

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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Database AWS ETL SQL 159

AWS Machine Learning Blog

FEBRUARY 29, 2024

is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. Instance types For our evaluation, we considered two comparable AWS CPU instances: a c6i.8xlarge 8xlarge powered by AWS Graviton3.

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AWS Deep Learning Deep Learning ML 121

Flipboard

JANUARY 14, 2025

The research team at AWS has worked extensively on building and evaluating the multi-agent collaboration (MAC) framework so customers can orchestrate multiple AI agents on Amazon Bedrock Agents. At AWS, he led the Dialog2API project, which enables large language models to interact with the external environment through dialogue.

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AWS Natural Language Processing AI AI 162

AWS Machine Learning Blog

DECEMBER 2, 2024

O Texts (2018). [3] Before joining AWS, he worked in the management consulting industry as a data scientist, serving the financial services and telecommunications sectors. Before joining AWS, he completed a PhD in Machine Learning at the Technical University of Munich, Germany, doing research on probabilistic models for event data.

Machine Learning 76

Machine Learning Machine Learning Deep Learning Deep Learning 76

AWS Machine Learning Blog

NOVEMBER 11, 2024

Prerequisites To try out this solution using SageMaker JumpStart, you’ll 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. Default for Meta Llama 3.2 1B and Meta Llama 3.2 3B is False. Default for Meta Llama 3.2

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AI AI ML ML 105

AWS Machine Learning Blog

NOVEMBER 18, 2024

MSD collaborated with AWS Generative Innovation Center (GenAIIC) to implement a powerful text-to-SQL generative AI solution that streamlines data extraction from complex healthcare databases. If you’re interested in working with the AWS Generative AI Innovation Center, reach out to the GenAIIC. He received a Ph.D.

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SQL Database AWS AI 97