AWS Machine Learning Blog

JULY 17, 2023

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon Redshift is a fully managed, fast, secure, and scalable cloud data warehouse.

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AWS Machine Learning Blog

MAY 1, 2024

Virginia) AWS Region. The diagram details a comprehensive AWS Cloud-based setup within a specific Region, using multiple AWS services. Queries made through this interface activate the AWS Lambda Invocation function, which interfaces with an agent. file for deploying the solution using the AWS CDK.

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Smart Data Collective

AUGUST 17, 2022

A hybrid cloud system is a cloud deployment model combining different cloud types, using both an on-premise hardware solution and a public cloud. Amazon’s AWS Glue is one such tool that allows you to consume data from Apache Kafka and Amazon-managed streaming for Apache Kafka (MSK).

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IBM Journey to AI blog

NOVEMBER 2, 2023

According to a Cloud Native Computing Foundation (CNCF) report (link resides outside ibm.com), Kubernetes is the second largest open-source project in the world after Linux and the primary container orchestration tool for 71% of Fortune 100 companies. Control plane nodes , which control the cluster.

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Dataconomy

APRIL 26, 2023

This is particularly true in the field of edge computing, where the need for innovative solutions has never been more pressing. We will review some of the current market leaders in edge computing and examine the unique strengths that have helped them rise to the top of the industry.

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AWS Machine Learning Blog

AUGUST 8, 2023

You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. With interactive sessions, you can choose Apache Spark or Ray to easily process large datasets, without worrying about cluster management.

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Pickl AI

DECEMBER 26, 2024

High-Performance Computing (HPC) Clusters These clusters combine multiple GPUs or TPUs to handle extensive computations required for training large generative models. How Does Cloud Computing Support Generative AI?

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

Organizations that want to build their own models or want granular control are choosing Amazon Web Services (AWS) because we are helping customers use the cloud more efficiently and leverage more powerful, price-performant AWS capabilities such as petabyte-scale networking capability, hyperscale clustering, and the right tools to help you build.

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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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AWS Machine Learning Blog

AUGUST 9, 2024

This post provides an overview of a custom solution developed by the AWS Generative AI Innovation Center (GenAIIC) for Deltek , a globally recognized standard for project-based businesses in both government contracting and professional services. For technical support or to contact AWS generative AI specialists, visit the GenAIIC webpage.

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IBM Journey to AI blog

NOVEMBER 13, 2023

Nodes run the pods and are usually grouped in a Kubernetes cluster, abstracting the underlying physical hardware resources. As an open-source system, Kubernetes services are supported by all the leading public cloud providers, including IBM, Amazon Web Services (AWS), Microsoft Azure and Google.

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The MLOps Blog

MARCH 23, 2023

Even for basic inference on LLM, multiple accelerators or multi-node computing clusters like multiple Kubernetes pods are required. But the issue we found was that MP is efficient in single-node clusters, but in a multi-node setting, the inference isn’t efficient. For instance, a 1.5B

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Data Science Fundamentals Going beyond knowing machine learning as a core skill, knowing programming and computer science basics will show that you have a solid foundation in the field. Computer science, math, statistics, programming, and software development are all skills required in NLP projects.

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phData

FEBRUARY 21, 2025

In this post, we will be particularly interested in the impact that cloud computing left on the modern data warehouse. The Cloud represents an iteration beyond the on-prem data warehouse, where computing resources are delivered over the Internet and are managed by a third-party provider.

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Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow.

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AWS Machine Learning Blog

OCTOBER 30, 2024

These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. About the Authors Kara Yang is a Data Scientist at AWS Professional Services in the San Francisco Bay Area, with extensive experience in AI/ML.

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Pickl AI

NOVEMBER 28, 2024

Familiarity with cloud computing tools supports scalable model deployment. Key techniques in unsupervised learning include: Clustering (K-means) K-means is a clustering algorithm that groups data points into clusters based on their similarities. It’s often used in customer segmentation and anomaly detection.

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Pickl AI

MAY 15, 2024

You can adopt these strategies as well as focus on continuous learning to upscale your knowledge and skill set. Leverage Cloud Platforms Cloud platforms like AWS, Azure, and GCP offer a suite of scalable and flexible services for data storage, processing, and model deployment.

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Becoming Human

MAY 15, 2023

The two most common types of unsupervised learning are clustering , where the algorithm groups similar data points together, and dimensionality reduction , where the algorithm reduces the number of features in the data. It is highly configurable and can integrate with other tools like Git, Docker, and AWS.

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Pickl AI

NOVEMBER 4, 2024

Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers. Cloud Platforms Cloud platforms have revolutionised Data Engineering by providing scalable resources and services that enhance data management capabilities.

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The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

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AWS Machine Learning Blog

AUGUST 14, 2023

His mission is to guarantee that as we continue on an ambitious journey to profoundly transform how cloud computing is used and perceived, we keep our feet well on the ground continuing the rapid growth we have enjoyed up until now. If you don’t have quota for that instance, request a quota increate on the AWS Service Quotas console.

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Flipboard

APRIL 10, 2025

John Blackledge, a tech analyst at TD Cowen, told the WSJ that the cloud computing division of e-commerce giant, Amazon Web Services, has historically earned $4 in incremental revenue for every $1 spent. But last year, AWS reported an operating income of $39.8 billion, and it's been a cash cow long before the AI boom.

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Pickl AI

JANUARY 29, 2025

Summary: The evolution of cloud computing has revolutionised data storage, accessibility, and IT infrastructure. From early computing models to modern cloud services, businesses now enjoy scalable, cost-efficient, and flexible solutions. The global cloud computing market, projected to grow from USD 626.4

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Pickl AI

MARCH 28, 2025

Summary: Load balancing in cloud computing optimises performance by evenly distributing traffic across multiple servers. With various algorithms and techniques, businesses can enhance cloud efficiency. Introduction Cloud computing is taking over the business world, and theres no slowing down! annual rate.

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AWS Machine Learning Blog

DECEMBER 18, 2024

Training an LLM is a compute-intensive and complex process, which is why Fastweb, as a first step in their AI journey, used AWS generative AI and machine learning (ML) services such as Amazon SageMaker HyperPod. The team opted for fine-tuning on AWS.

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ODSC - Open Data Science

SEPTEMBER 12, 2024

Commerce Department has announced a new proposal aimed at enhancing the safety and security of advanced AI technologies and cloud computing services. The proposed rule aims to establish clear and enforceable reporting requirements to better monitor and manage the risks associated with advanced AI models and computing clusters.

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Data Science Dojo

FEBRUARY 21, 2025

Scalability with Dojo ExaPODs The highest level of Teslas Dojo architecture is the Dojo ExaPod – a complete supercomputing cluster. The ExaPOD configuration allows Tesla to scale Dojos computational capabilities by deploying multiple ExaPODs. Two such racks form a cabinet which are further combined to form the ExaPODs.

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AWS Machine Learning Blog

NOVEMBER 6, 2024

Transcribe audio with Amazon Transcribe In this case, we use an AWS re:Invent 2023 technical talk as a sample. This was driven by strong operational performance across all three reportable segments (North America, International, and AWS) and better-than-expected operating leverage, including lower cost to serve. AWS revenue grew 17.2%

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