AWS Machine Learning Blog

OCTOBER 2, 2023

A successful deployment of a machine learning (ML) model in a production environment heavily relies on an end-to-end ML pipeline. Although developing such a pipeline can be challenging, it becomes even more complex when dealing with an edge ML use case. This helps avoid costly defects at later stages of the production process.

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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

APRIL 18, 2025

Think of the examples of clickstream data, credit card swipes, Internet of Things (IoT) sensor data, log analysis and commodity priceswhere both current data and historical trends are important to make a learned decision. On the next screen, review your selections. To finalize the setup, choose Create.

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AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. On top of that, the whole process can be configured and managed via the AWS SDK, which is what we use to orchestrate our labeling workflow as part of our CI/CD pipeline.

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AWS Machine Learning Blog

APRIL 24, 2024

With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. It is hosted on Amazon Elastic Container Service (Amazon ECS) with AWS Fargate , and it is accessed using an Application Load Balancer. It serves as the data source to the knowledge base.

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AWS Machine Learning Blog

APRIL 12, 2023

In this post, we describe how AWS Partner Airis Solutions used Amazon Lookout for Equipment , AWS Internet of Things (IoT) services, and CloudRail sensor technologies to provide a state-of-the-art solution to address these challenges. It’s an easy way to run analytics on IoT data to gain accurate insights.

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AWS Machine Learning Blog

APRIL 18, 2023

Amazon Monitron is an end-to-end condition monitoring solution that enables you to start monitoring equipment health with the aid of machine learning (ML) in minutes, so you can implement predictive maintenance and reduce unplanned downtime. For the detailed Amazon Monitron installation guide, refer to Getting started with Amazon Monitron.

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AWS Machine Learning Blog

OCTOBER 19, 2023

For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). This post demonstrates how to use Amazon SageMaker Data Wrangler and Amazon Comprehend to automatically redact PII from tabular data as part of your machine learning operations (ML Ops) workflow.

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AWS Machine Learning Blog

MARCH 29, 2023

there is enormous potential to use machine learning (ML) for quality prediction. ML-based predictive quality in HAYAT HOLDING HAYAT is the world’s fourth-largest branded baby diapers manufacturer and the largest paper tissue manufacturer of the EMEA. After the data preparation phase, a two-stage approach is used to build the ML models.

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AWS Machine Learning Blog

MAY 10, 2023

Sleepme is an industry leader in sleep temperature management and monitoring products, including an Internet of Things (IoT) enabled sleep tracking sensor suite equipped with heart rate, respiration rate, bed and ambient temperature, humidity, and pressure sensors. This use case demanded an ML model that served real-time inference.

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AWS Machine Learning Blog

MARCH 19, 2024

In the evolving landscape of manufacturing, the transformative power of AI and machine learning (ML) is evident, driving a digital revolution that streamlines operations and boosts productivity. However, this progress introduces unique challenges for enterprises navigating data-driven solutions.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

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Dataconomy

JULY 11, 2024

Machine learning frameworks Frameworks like TensorFlow Lite and Core ML allow developers to integrate machine learning models into mobile apps. Internet of Things (IoT) integration IoT platforms The integration of IoT in mobile apps is expanding, with platforms like AWS IoT and Azure IoT offering robust solutions.

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Smart Data Collective

JANUARY 10, 2023

Usually, companies choose a platform provided by one of the most well-known vendors like Amazon (AWS), Google Cloud, or Microsoft Azure. Today there are various tools that rely on ML and AI technologies which help them to understand the received data and further present them in a convenient format.

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AWS Machine Learning Blog

JANUARY 20, 2023

In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned. The challenge CCC processes more than $1 trillion claims transactions annually.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

You’ll need access to an AWS account with an access key or AWS Identity and Access Management (IAM) role with permissions to Amazon Bedrock and Amazon Location. You may need to run aws configure --profile and set a default Region; this application was tested using us-east-1. aws:/root/.aws

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IBM Journey to AI blog

MARCH 20, 2024

Amazon Web Services (AWS), Google Cloud Platform, IBM Cloud or Microsoft Azure) makes computing resources (e.g., ready-to-use software applications, virtual machines (VMs) , enterprise-grade infrastructures and development platforms) available to users over the public internet on a pay-per-usage basis. What is a public cloud?

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Pickl AI

MARCH 1, 2023

Cloud computing is a way to use the internet to access different types of technology services. These services include things like virtual machines, storage, databases, networks, and tools for artificial intelligence and the Internet of Things. Thus making the system impervious to hackers.

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IBM Journey to AI blog

FEBRUARY 6, 2024

Internet companies like Amazon led the charge with the introduction of Amazon Web Services (AWS) in 2002, which offered businesses cloud-based storage and computing services, and the launch of Elastic Compute Cloud (EC2) in 2006, which allowed users to rent virtual computers to run their own applications. Google Workspace, Salesforce).

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IBM Journey to AI blog

SEPTEMBER 11, 2023

Db2 can run on Red Hat OpenShift and Kubernetes environments, ROSA & EKS on AWS, and ARO & AKS on Azure deployments. The ability to ingest hundreds of thousands of rows each second is critical for more and more applications, particularly for mobile computing and the Internet of Things (IoT).

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IBM Journey to AI blog

AUGUST 6, 2024

Today, all leading CSPs, including Amazon Web Services (AWS Lambda), Microsoft Azure (Azure Functions) and IBM (IBM Cloud Code Engine) offer serverless platforms. To accomplish this, enterprises are relying more than ever on cloud functions and reducing their dependence on on-premises infrastructure.

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Mlearning.ai

JULY 8, 2023

The source and target points can be of any storage service, for instance an Azure Blob Storage container, an AWS S3 bucket or a database system to name a few. For example, Internet of Things (IoT) devices broadcast data in a continuous manner, so in order to be able to monitor them we would need a streaming ETL.

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Dataconomy

APRIL 4, 2023

Best XaaS companies Here are some of the best Anything as a Service companies, in no particular order: Amazon Web Services (AWS):   AWS is a leading provider of cloud computing services, offering a wide range of XaaS solutions, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

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Dataconomy

APRIL 4, 2023

Best XaaS companies Here are some of the best Anything as a Service companies, in no particular order: Amazon Web Services (AWS):   AWS is a leading provider of cloud computing services, offering a wide range of XaaS solutions, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

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AWS Machine Learning Blog

NOVEMBER 27, 2024

Solution overview The chess demo uses a broad spectrum of AWS services to create an interactive and engaging gaming experience. On the frontend, AWS Amplify hosts a responsive React TypeScript application while providing secure user authentication through Amazon Cognito using the Amplify SDK. The demo offers a few gameplay options.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

This post describes how Agmatix uses Amazon Bedrock and AWS fully featured services to enhance the research process and development of higher-yielding seeds and sustainable molecules for global agriculture. AWS generative AI services provide a solution In addition to other AWS services, Agmatix uses Amazon Bedrock to solve these challenges.

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