O'Reilly Media

SEPTEMBER 19, 2023

A basic, production-ready cluster priced out to the low-six-figures. A company then needed to train up their ops team to manage the cluster, and their analysts to express their ideas in MapReduce. Plus there was all of the infrastructure to push data into the cluster in the first place. Goodbye, Hadoop. And it was good.

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Hadoop Algorithm ML ML 136

AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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AWS ML ML Clustering 121

AWS Machine Learning Blog

NOVEMBER 13, 2024

With Amazon OpenSearch Serverless, you don’t need to provision, configure, and tune the instance clusters that store and index your data. He is particularly passionate about AI/ML and enjoys building proof-of-concept solutions for his customers. For more information on managing credentials securely, see the AWS Boto3 documentation.

AWS 122

AWS Database K-nearest Neighbors AI 122

AWS Machine Learning Blog

MAY 10, 2023

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. However, there are scenarios in which data scientists may prefer to transition from interactive development on notebooks to batch jobs.

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AWS Machine Learning Blog

MARCH 10, 2025

Cost-efficiency and infrastructure optimization By moving away from GPU-based clusters to Fargate, our monthly infrastructure costs are now 78.47% lower, and our per-question costs have reduced by 87.6%. With seven years of experience in AI/ML, his expertise spans GenAI and NLP, specializing in designing and deploying agentic AI systems.

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AWS Database AI AI 118

Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM ” by Deepak Narayanan et al.

Natural Language Processing 98

Natural Language Processing Algorithm Machine Learning Machine Learning 98

AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. or later image versions.

SQL 133

SQL AWS Database Data Scientist 133

Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., 2012; Otsu, 1979; Long et al., Methodology In this study, we used the publicly available PASCAL VOC 2012 dataset (Everingham et al., References: Arbeláez, P.,

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

DrivenData Labs

MAY 1, 2025

For more practical guidance about extracting ML features from speech data, including example code to generate transformer embeddings, see this blog post ! Cluster 0 was in English and included many people talking to an Alexa. Cluster 1 and 2 were both Spanish. Cluster 3 was Mandarin. changes between 2003 and 2012).

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AWS Machine Learning Blog

DECEMBER 4, 2024

Scaling machine learning (ML) workflows from initial prototypes to large-scale production deployment can be daunting task, but the integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution to this challenge. Create a JupyterLab space and mount an Amazon FSx for Lustre file system to your space.

ML 103

ML ML Clustering AWS 103

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.

Database 108

Database AWS Clustering Data Lakes 108