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

AWS Machine Learning

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For a deep dive, refer to Cross account feature group discoverability and access.

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Churn prediction using multimodality of text and tabular features with Amazon SageMaker Jumpstart

AWS Machine Learning

Customer churn is a problem faced by a wide range of companies, from telecommunications to banking, where customers are typically lost to competitors. This post aims to build a model that can process and relate information from multiple modalities such as tabular and textual features.

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Everything You Need to Know About Auto Attendant

Hodusoft

Auto attendant, also known as Interactive Voice Responder (IVR) system is an advanced business phone system feature that automates and simplifies the incoming call process and routes the callers to the most appropriate agent or department. Pros of Using Auto Attendant Cons of Auto Attendant Auto Attendant Scripts – What to Record?

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Build custom code libraries for your Amazon SageMaker Data Wrangler Flows using AWS Code Commit

AWS Machine Learning

This post introduces a best practice for managing custom code within your Amazon SageMaker Data Wrangler workflow. It contains over 300 built-in data transformation steps to aid with feature engineering, normalization, and cleansing to transform your data without having to write any code. Choose Amazon S3 for Data sources.

Scripts 61
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MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

AWS Machine Learning

You’ll see how to use Amazon SageMaker Edge Manager , Amazon SageMaker Studio , and AWS IoT Greengrass v2 to create an MLOps (ML Operations) environment that automates the process of building and deploying ML models to large fleets of edge devices. In this case, the production environment consists of multiple fleets of edge devices.

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Build and train ML models using a data mesh architecture on AWS: Part 2

AWS Machine Learning

In part 1 , we addressed the data steward persona and showcased a data mesh setup with multiple AWS data producer and consumer accounts. The data scientists in this team use Amazon SageMaker to build and train a credit risk prediction model using the shared credit risk data product from the consumer banking LoB. Data exploration.

Scripts 70
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Unlock the potential of generative AI in industrial operations

AWS Machine Learning

Workers gain productivity through AI-generated insights, engineers can proactively detect anomalies, supply chain managers optimize inventories, and plant leadership makes informed, data-driven decisions. For details, refer to Step 1: Create your AWS account. For this tutorial, you need a bash terminal with Python 3.9