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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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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. It also provides common ML algorithms that are optimized to run efficiently against extremely large data in a distributed environment.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

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

AWS Machine Learning

This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In this post, we address the analytics and ML platform team as a consumer in the data mesh.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Use Amazon SageMaker Data Wrangler in Amazon SageMaker Studio with a default lifecycle configuration

AWS Machine Learning

If you use the default lifecycle configuration for your domain or user profile in Amazon SageMaker Studio and use Amazon SageMaker Data Wrangler for data preparation, then this post is for you. Data Wrangler supports standard data types such as CSV, JSON, ORC, and Parquet. For more information, see Jupyter Kernel Gateway.

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Same Tactics, Different Scripts: What Contact Center Fraud Sounds Like in the Age of Coronavirus

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Any disruption to the status quo provides an opportunity to seize sensitive consumer data and leverage it against individuals and their financial institutions. The New Fraud Scripts. In “normal” times, a fraudster’s script may have read something like this: . Travel-Related Inconveniences and Emergencies .

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