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AWS positioned in the Leaders category in the 2022 IDC MarketScape for APEJ AI Life-Cycle Software Tools and Platforms Vendor Assessment

AWS Machine Learning Blog

The recently published IDC MarketScape: Asia/Pacific (Excluding Japan) AI Life-Cycle Software Tools and Platforms 2022 Vendor Assessment positions AWS in the Leaders category. AWS met the criteria and was evaluated by IDC along with eight other vendors. AWS is positioned in the Leaders category based on current capabilities.

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How Marubeni is optimizing market decisions using AWS machine learning and analytics

AWS Machine Learning Blog

This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. In this post, you will learn how Marubeni is optimizing market decisions by using the broad set of AWS analytics and ML services, to build a robust and cost-effective Power Bid Optimization solution.

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How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

AWS Machine Learning Blog

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

For more information on Mixtral-8x7B Instruct on AWS, refer to Mixtral-8x7B is now available in Amazon SageMaker JumpStart. Before you get started with the solution, create an AWS account. This identity is called the AWS account root user. For more detailed steps to prepare the data, refer to the GitHub repo.

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Deploy large language models for a healthtech use case on Amazon SageMaker

AWS Machine Learning Blog

Traditional manual processing of adverse events is made challenging by the increasing amount of health data and costs. Overall, $384 billion is projected as the cost of pharmacovigilance activities to the overall healthcare industry by 2022. We implemented the solution using the AWS Cloud Development Kit (AWS CDK).

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Simplify continuous learning of Amazon Comprehend custom models using Comprehend flywheel

AWS Machine Learning Blog

We will also illustrate how flywheel can be used to orchestrate the training of a new model version and improve the accuracy of the model using new labeled data. Optional) Configure permissions for AWS KMS keys for AWS KMS keys for the datalake. Create a data access role that authorizes Amazon Comprehend to access the datalake.