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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning

One aspect of this data preparation is feature engineering. Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it.

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Schneider Electric leverages Retrieval Augmented LLMs on SageMaker to ensure real-time updates in their ERP systems

AWS Machine Learning

This post was co-written with Anthony Medeiros, Manager of Solutions Engineering and Architecture for North America Artificial Intelligence, and Blake Santschi, Business Intelligence Manager, from Schneider Electric. In particular, they are routinely used to store information related to customer accounts.

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Build a foundation model (FM) powered customer service bot with agents for Amazon Bedrock

AWS Machine Learning

The action is an API that the model can invoke from an allowed set of APIs. Components in agents for Amazon Bedrock Behind the scenes, agents for Amazon Bedrock automate the prompt engineering and orchestration of user-requested tasks. It, then, uses the API schema to invoke corresponding code in the Lambda function.

APIs 87
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Wipro has used the input filter and join functionality of SageMaker batch transformation API.

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How Yara is using MLOps features of Amazon SageMaker to scale energy optimization across their ammonia plants

AWS Machine Learning

If the current energy consumption deviates too much from the optimal point, ELC provides an action to adjust internal process variables to optimize energy efficiency based on analytical models. Yara has built APIs using Amazon API Gateway to expose the sensor data to applications such as ELC. ELC is hosted in the cloud.

APIs 92
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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning

The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Purina’s solution is deployed as an API Gateway HTTP endpoint, which routes the requests to obtain pet attributes.

APIs 96