AWS Machine Learning Blog

SEPTEMBER 29, 2023

In late 2023, Planet announced a partnership with AWS to make its geospatial data available through Amazon SageMaker. In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Planet’s data is therefore a valuable resource for geospatial ML.

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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. It involves training a global machine learning (ML) model from distributed health data held locally at different sites.

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AWS Machine Learning Blog

AUGUST 27, 2024

Currently, users might have to engineer their applications to handle scenarios involving traffic spikes that can use service quotas from multiple regions by implementing complex techniques such as client-side load balancing between AWS regions, where Amazon Bedrock service is supported. Become more resilient to any traffic bursts.

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DagsHub

JULY 25, 2024

This guarantees businesses can fully utilize deep learning in their AI and ML initiatives. You can make more informed judgments about your AI and ML initiatives if you know these platforms' features, applications, and use cases. Developed by François Chollet, it was released in 2015 to simplify the creation of deep learning models.

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AWS Machine Learning Blog

AUGUST 11, 2023

Natural language processing (NLP) is the field in machine learning (ML) concerned with giving computers the ability to understand text and spoken words in the same way as human beings can. Note that by following the steps in this section, you will deploy infrastructure to your AWS account that may incur costs.

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AWS Machine Learning Blog

FEBRUARY 28, 2023

In today’s highly competitive market, performing data analytics using machine learning (ML) models has become a necessity for organizations. For example, in the healthcare industry, ML-driven analytics can be used for diagnostic assistance and personalized medicine, while in health insurance, it can be used for predictive care management.

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AWS Machine Learning Blog

MAY 15, 2023

Getir was founded in 2015 and operates in Turkey, the UK, the Netherlands, Germany, France, Spain, Italy, Portugal, and the United States. We outline how we built an automated demand forecasting pipeline using Forecast and orchestrated by AWS Step Functions to predict daily demand for SKUs.

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IBM Journey to AI blog

NOVEMBER 13, 2023

In 2015, Google donated Kubernetes as a seed technology to the Cloud Native Computing Foundation (CNCF) (link resides outside ibm.com), the open-source, vendor-neutral hub of cloud-native computing. And Kubernetes can scale ML workloads up or down to meet user demands, adjust resource usage and control costs.

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AWS Machine Learning Blog

NOVEMBER 21, 2023

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. At this event, SPIE member Light and Light-based Technologies (IYL 2015). The endorsement for a Day of Light has been embraced by SPIE and other founding partners of IYL 2015.

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AWS Machine Learning Blog

APRIL 18, 2023

JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few steps. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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AWS Machine Learning Blog

APRIL 18, 2023

JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few steps. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Meesho was founded in 2015 and today focuses on buyers and sellers across India. We used AWS machine learning (ML) services like Amazon SageMaker to develop a powerful generalized feed ranker (GFR). In the following sections, we discuss each component and the AWS services used in more detail.

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Snorkel AI

MAY 24, 2023

Our speakers lead their fields and embody the desire to create revolutionary ML experiences by leveraging the power of data-centric AI to drive innovation and progress. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. He was previously a senior leader at AWS, and the CTO of Analytics & ML at IBM.

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Snorkel AI

MAY 24, 2023

Our speakers lead their fields and embody the desire to create revolutionary ML experiences by leveraging the power of data-centric AI to drive innovation and progress. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. He was previously a senior leader at AWS, and the CTO of Analytics & ML at IBM.

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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

OCTOBER 23, 2024

SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). At its core, Amazon Bedrock provides the foundational infrastructure for robust performance, security, and scalability for deploying machine learning (ML) models.

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