AWS Machine Learning Blog

NOVEMBER 14, 2024

This post explores a solution that uses the power of AWS generative AI capabilities like Amazon Bedrock and OpenSearch vector search to perform damage appraisals for insurers, repair shops, and fleet managers. Specific instructions can be found on the AWS Samples repository. On the AWS CloudFormation console, delete the stack.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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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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The MLOps Blog

NOVEMBER 30, 2022

In this article, you will learn about: the challenges plaguing the ML space and why conventional tools are not the right answer to them. ML model versioning: where are we at? Starting from AlexNet with 8 layers in 2012 to ResNet with 152 layers in 2015 – the deep neural networks have become deeper with time.

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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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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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Towards AI

APRIL 19, 2024

Amazon (AWS) 👉Industry domain: Online retail and web services provider 👉Location: Over 175 Amazon fulfillment centers globally 👉Year founded: 1994 👉Key Products developed: Amazon Bedrock, Q, Code Whisperer, Sage Maker 👉Benefits: Fully managed generative AI service options, AWS free tier for experimentation 7.

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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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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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Women in Big Data

MAY 21, 2024

Launched in 2015 and becoming a nonprofit organization in 2020, WiBD is a grassroots initiative dedicated to inspiring, connecting, and advancing women in data fields. Currently, there is an ML Engineer Track, but no certification is available yet. We provided a quick overview of Women in Big Data (WiBD). link] com/certification.

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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

NOVEMBER 11, 2024

Solution overview SageMaker JumpStart is a robust feature within the SageMaker machine learning (ML) environment, offering practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs). An AWS Identity and Access Management (IAM) role to access SageMaker. You can access the Meta Llama 3.2

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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 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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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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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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