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Building ML Model in AWS Sagemaker

Analytics Vidhya

The post Building ML Model in AWS Sagemaker appeared first on Analytics Vidhya. AI/ML has become an integral part of research and innovations. The main objective of the AI system is to solve real-world problems where […].

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Deployment of ML models in Cloud – AWS SageMaker?(in-built algorithms)

Analytics Vidhya

Introduction: Gone are the days when enterprises set up their own in-house server and spending a gigantic amount of budget on storage infrastructure & The post Deployment of ML models in Cloud – AWS SageMaker?(in-built in-built algorithms) appeared first on Analytics Vidhya.

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Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

AWS Machine Learning Blog

In this post, we describe the end-to-end workforce management system that begins with location-specific demand forecast, followed by courier workforce planning and shift assignment using Amazon Forecast and AWS Step Functions. AWS Step Functions automatically initiate and monitor these workflows by simplifying error handling.

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Powering the future: The synergy of IBM and AWS partnership

IBM Journey to AI blog

IBM and AWS have been working together since 2016 to provide secure, automated solutions for hybrid cloud environments. Data is the fuel that powers AI algorithms, enabling them to generate insights, predictions, and solutions that drive businesses forward. Watsonx.data on AWS: Imagine having the power of data at your fingertips.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process. Wipro is an AWS Premier Tier Services Partner and Managed Service Provider (MSP).

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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

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In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. Run the AWS Glue ML transform job.

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Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

Customers often need to train a model with data from different regions, organizations, or AWS accounts. Existing partner open-source FL solutions on AWS include FedML and NVIDIA FLARE. These open-source packages are deployed in the cloud by running in virtual machines, without using the cloud-native services available on AWS.

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