AWS Machine Learning

JANUARY 5, 2024

Artificial intelligence (AI) and machine learning (ML) offerings from Amazon Web Services (AWS) , along with integrated monitoring and notification services, help organizations achieve the required level of automation, scalability, and model quality at optimal cost. The pipeline should also be launched by the data push event to S3.

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AWS Machine Learning

APRIL 19, 2023

Our original plan was to also host the models in Python and utilize gRPC to communicate with another service, which will use the Java gRPC client to send the request. Mainly, we saw the network overhead between two different services running in separate run environments or pods, which resulted in higher latency.

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AWS Machine Learning

SEPTEMBER 20, 2023

Another example is independent software vendors (ISVs) that make their products and services available in different cloud platforms to benefit their end customers. We train the model using Amazon SageMaker, store the model artifacts in Amazon Simple Storage Service (Amazon S3), and deploy and run the model in Azure. The Azure CLI.

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AWS Machine Learning

JANUARY 9, 2024

To implement the solution, we use SageMaker, a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. For more information about the various deployment options SageMaker provides, refer to Amazon SageMaker Model Hosting FAQs.

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AWS Machine Learning

FEBRUARY 1, 2023

AWS customers often deploy these workloads using Amazon Elastic Kubernetes Service (Amazon EKS). Amazon EKS is a managed Kubernetes service that simplifies the creation, configuration, lifecycle, and monitoring of Kubernetes clusters while still offering the full flexibility of upstream Kubernetes.

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AWS Machine Learning

APRIL 23, 2024

Any existing SageMaker Studio apps will need to be restarted for the Docker service update to take effect. amazonaws.com/myflaskapp:latest Test Docker images Having Docker installed inside a JupyterLab (or Code Editor) SageMaker Studio space allows you to test pre-built or custom Docker images as containers (or containerized applications).

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AWS Machine Learning

APRIL 1, 2024

Amazon Elastic Kubernetes Service (Amazon EKS) is a popular Kubernetes-conformant service that greatly simplifies the process of running AI/ML workloads, making it more manageable and less time-consuming. About the Authors Kanwaljit Khurmi is a Principal AI/ML Solutions Architect at Amazon Web Services.

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AWS Machine Learning

MARCH 15, 2023

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. Step Functions is a serverless workflow service that can control SageMaker APIs directly through the use of the Amazon States Language.

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AWS Machine Learning

APRIL 19, 2024

In this post, we focus on how we used Karpenter on Amazon Elastic Kubernetes Service (Amazon EKS) to scale AI training and inference, which are core elements of the Iambic discovery platform. The architecture deploys a simple service in a Kubernetes pod within an EKS cluster. The service is exposed behind a reverse-proxy using Traefik.

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AWS Machine Learning

MAY 31, 2023

Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that simplifies your deployment, management, and scaling of containerized applications. Prerequisites To follow along, familiarity with core AWS services such as Amazon EC2 and Amazon ECS is implied.

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AWS Machine Learning

AUGUST 16, 2023

The original concept came out of an AI/ML Hackathon supported by Simone Zucchet (AWS Solutions Architect) and Tim Precious (AWS Account Manager) and was developed into production using AWS services in under 6 weeks with support from AWS. Building the Open Arena Building the Open Arena was a multi-faceted process.

Enterprise 74

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ConvergeOne

JULY 19, 2022

An IVA has many more capabilities and brings more value to address customer experience and/or self-service business strategies. An IVA, on the other hand, is generally a cloud-native, containerized application based on low-code or no-code development. IVR vs. IVA. When you compare the two systems, they are vastly different.

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AWS Machine Learning

OCTOBER 19, 2022

RStudio on amazon SageMaker is a managed service option which allows you to avoid having to manage the underlying infrastructure for your RStudio environment. Fig1: Architecture Diagram showing the interaction of various AWS Services. Install Paws (see guidance earlier in this post) so that you can access the appropriate AWS services.

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AWS Machine Learning

JULY 15, 2022

The ability that developers and data scientists have to quickly build, train, and deploy machine learning (ML) models at scale on the cloud with services like Amazon SageMaker. In the following example, the model classified a tweet from Amazon Web Services in the machine learning category, with a confidence score of 97%, generating an alert.

Engineering 84

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AWS Machine Learning

JUNE 7, 2023

The workshop shows the distributed training setup using AWS Batch and in particular, the multi-node parallel jobs feature to launch large-scale containerized training jobs on fully managed clusters. He focuses on large-scale ML workloads across AWS services like Amazon EKS, Amazon ECS, Elastic Fabric Adapter, AWS Batch, and Amazon SageMaker.

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NICE inContact

DECEMBER 20, 2018

There is evolution and nuances in the technology core like containerization, micro service architecture and others. The next evolved option is SaaS or cloud native where it is truly multi-tenant and a single instance of software and infrastructure can support multiple customers. But it is good to start with these broad categories.

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Contact Center Cloud contact Enterprise Interactive Voice Response 54

ViiBE Blog

JANUARY 22, 2021

ViiBE is the web technology of video assistance and ticketing system designed for technical support and after-sales services. Before ViiBE, I worked as a Telecom business engineer for the Orange Business Service account in Paris. How did you come up with the idea for ViiBE? How has the pandemic impacted your approach?

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virtual call center Technical Support Engineering Self service 52

AWS Machine Learning

JUNE 6, 2023

support is not limited to the services and compute shown in example use-case in this post; it extends to many others on AWS, which we discuss in this section. Choice of ML services To use AWS compute, you can select from a broad set of global cloud-based services for ML development, compute, and workflow orchestration.

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Serenova

FEBRUARY 6, 2019

When we developed CxEngage, our next-generation contact center-as-as-service solution, we knew it was time to take an approach to development that provided our customers the agility and resiliency that contact centers need today. Enables each service in the application to be tested, deployed and scaled independently.

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AWS Machine Learning

SEPTEMBER 9, 2022

athenahealth a leading provider of network-enabled software and services for medical groups and health systems nationwide. In the artificial intelligence (AI) space, athenahealth uses data science and machine learning (ML) to accelerate business processes and provide recommendations, predictions, and insights across multiple services.

Engineering 75

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SmartKarrot

APRIL 12, 2022

‘The ones using big data, modern machine learning, and other advanced analytics technologies to, directly and indirectly, enhance IT operations (monitoring, automation, and service desk) functions with proactive, personal, and dynamic insight. For instance, modernization is a key quality to implement microservices and containerization.

Analytics 10

Analytics Big data SaaS Technology 10

CSM Magazine

MAY 5, 2022

Public cloud storage has made these services available to a large audience regardless of their physical location.Net software solutions can facilitate cloud development greatly. The systems for containerized applications such as Kubernetes are gaining momentum at the moment. Edge Computing. Containers.

Healthcare 52

Healthcare Transportation Enterprise Chatbots 52

AWS Machine Learning

NOVEMBER 1, 2022

Pieces services also provide insights to healthcare providers in plain language and optimize clarity of patients’ clinical issues to help care teams work more efficiently. Adarga uses Amazon EC2, OpenSearch Service, Amazon Aurora , Amazon DocumentDB (with MongoDB compatibility) , Amazon Translate , and SageMaker.

Healthcare 63

Healthcare Construction Technology Enterprise 63

AWS Machine Learning

OCTOBER 1, 2022

Kubernetes is a widely adopted system for automating infrastructure deployment, resource scaling, and management of these containerized applications. Use the Kubeflow Pipelines SDK client to call the pipeline service endpoint to run the pipeline.

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AWS Machine Learning

NOVEMBER 1, 2023

Addressing complex challenges like image-to-text segmentation, we use the containerized Grounded Segment Anything module, synergizing with the Grounded DINO and Segment Anything Model (SAM) mechanism for text-driven object detection and segmentation. The following figure illustrates a VLP pipeline data flow and service components.

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AWS Machine Learning

SEPTEMBER 7, 2023

In February 2022, Amazon Web Services added support for NVIDIA GPU metrics in Amazon CloudWatch , making it possible to push metrics from the Amazon CloudWatch Agent to Amazon CloudWatch and monitor your code for optimal GPU utilization. To enable access to Kubernetes services using port-forwarding, you will also need kubectl.

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Metrics APIs Management Engineering 86

AWS Machine Learning

NOVEMBER 30, 2023

As democratization of foundation models (FMs) becomes more prevalent and demand for AI-augmented services increases, software as a service (SaaS) providers are looking to use machine learning (ML) platforms that support multiple tenants—for data scientists internal to their organization and external customers.

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AWS Machine Learning

SEPTEMBER 2, 2022

In this post, we provide an example architecture to train PyTorch models using the Torch Distributed Elastic framework in a distributed data parallel fashion using Amazon Elastic Kubernetes Service (Amazon EKS). Amazon EKS is a managed container service to run and scale Kubernetes applications on AWS. Prerequisites. EKS clusters.

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