2014, AWS and Clustering - Data Science Current

Implement smart document search index with Amazon Textract and Amazon OpenSearch

AWS Machine Learning Blog

SEPTEMBER 8, 2023

We’ll cover how technologies such as Amazon Textract, AWS Lambda , Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service can be integrated into a workflow that seamlessly processes documents. The main concepts used are the AWS Cloud Development Kit (CDK) constructs, the actual CDK stacks and AWS Step Functions.

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Understanding and predicting urban heat islands at Gramener using Amazon SageMaker geospatial capabilities

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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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

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. In the first post , we described FL concepts and the FedML framework.

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How to Achieve High-Accuracy Results When Using LLMs

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The history of Kubernetes

IBM Journey to AI blog

NOVEMBER 2, 2023

These tech pioneers were looking for ways to bring Google’s internal infrastructure expertise into the realm of large-scale cloud computing and also enable Google to compete with Amazon Web Services (AWS)—the unrivaled leader among cloud providers at the time. Control plane nodes , which control the cluster.

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Clustering Cloud Computing AWS

Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care.

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Top 6 Kubernetes use cases

IBM Journey to AI blog

NOVEMBER 13, 2023

Developed internally at Google and released to the public in 2014, Kubernetes has enabled organizations to move away from traditional IT infrastructure and toward the automation of operational tasks tied to the deployment, scaling and managing of containerized applications (or microservices ).

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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning Blog

MAY 7, 2024

Founded in 2014, Veritone empowers people with AI-powered software and solutions for various applications, including media processing, analytics, advertising, and more. The processed videos are sent to AWS services like Amazon Rekognition, Amazon Transcribe, and Amazon Comprehend to generate metadata at shot level and video level.

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Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

AWS Machine Learning Blog

JULY 13, 2023

Amazon SageMaker distributed training jobs enable you with one click (or one API call) to set up a distributed compute cluster, train a model, save the result to Amazon Simple Storage Service (Amazon S3), and shut down the cluster when complete. Finally, launching clusters can introduce operational overhead due to longer starting time.

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How to choose a graph database: we compare 6 favorites

Cambridge Intelligence

OCTOBER 19, 2023

.” First release: 2017 Format: An open-source, hosted, native, property and RDF graph database Top 3 advantages: Built for cloud – Neptune is fully managed by AWS, meaning you can leave infrastructure challenges, updates, backups and other admin tasks to them.

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7 Best Machine Learning Workflow and Pipeline Orchestration Tools 2024

DagsHub

APRIL 7, 2024

The project was created in 2014 by Airbnb and has been developed by the Apache Software Foundation since 2016. Cloud-agnostic and can run on any Kubernetes cluster. Integration: It can work alongside other workflow orchestration tools (Airflow cluster or AWS SageMaker Pipelines, etc.)

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

OCTOBER 23, 2024

Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers. BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. Our model achieves 28.4 after training for 3.5

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Must-Have Prompt Engineering Skills for 2024

ODSC - Open Data Science

JANUARY 29, 2024

These outputs, stored in vector databases like Weaviate, allow Prompt Enginers to directly access these embeddings for tasks like semantic search, similarity analysis, or clustering. GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN.

Data Science

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Best Egg achieved three times faster ML model training with Amazon SageMaker Automatic Model Tuning

AWS Machine Learning Blog

JANUARY 26, 2023

Since March 2014, Best Egg has delivered $22 billion in consumer personal loans with strong credit performance, welcomed almost 637,000 members to the recently launched Best Egg Financial Health platform, and empowered over 180,000 cardmembers who carry the new Best Egg Credit Card in their wallet. He is also a skilled origamist.

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Faster distributed graph neural network training with GraphStorm v0.4

AWS Machine Learning Blog

FEBRUARY 11, 2025

Although GraphStorm can run efficiently on single instances for small graphs, it truly shines when scaling to enterprise-level graphs in distributed mode using a cluster of Amazon Elastic Compute Cloud (Amazon EC2) instances or Amazon SageMaker. Today, AWS AI released GraphStorm v0.4. This dataset has approximately 170,000 nodes and 1.2

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Perform batch transforms with Amazon SageMaker Jumpstart Text2Text Generation large language models

AWS Machine Learning Blog

MAY 24, 2023

Batch transform is cost-effective because unlike real-time hosted endpoints that have persistent hardware, batch transform clusters are torn down when the job is complete and therefore the hardware is only used for the duration of the batch job. Rachna Chadha is a Principal Solutions Architect AI/ML in Strategic Accounts at AWS.

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Data Science Current

Implement smart document search index with Amazon Textract and Amazon OpenSearch

Understanding and predicting urban heat islands at Gramener using Amazon SageMaker geospatial capabilities

Webinars

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

Webinars

The history of Kubernetes

Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

Top 6 Kubernetes use cases

How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

How to choose a graph database: we compare 6 favorites

7 Best Machine Learning Workflow and Pipeline Orchestration Tools 2024

How to Manage Unstructured Data in AI and Machine Learning Projects

Must-Have Prompt Engineering Skills for 2024

Best Egg achieved three times faster ML model training with Amazon SageMaker Automatic Model Tuning

Faster distributed graph neural network training with GraphStorm v0.4

Perform batch transforms with Amazon SageMaker Jumpstart Text2Text Generation large language models

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