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Anagha Barve

As seen in: Amazon Web Services
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Articles

Detecting fraud patterns across Snowflake and AWS using SageMaker Data Agent

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By Akash Gupta, Anagha Barve, Siddharth Gupta
| Amazon Web Services
Financial services organizations increasingly run analytical workloads across multiple systems. For example, customers typically store transaction records in Snowflake for its concurrency handling during peak volumes, while they store risk scores, customer profiles, and behavioral signals on AWS.
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Amazon SageMaker introduces Amazon S3 based shared storage for enhanced project collaboration | Amazon Web Services

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By Hari Ramesh, Anagha Barve, Anchit Gupta, Saurabh Bhutyani
| Amazon Web Services
AWS recently announced that Amazon SageMaker now offers Amazon Simple Storage Service (Amazon S3) based shared storage as the default project file storage option for new Amazon SageMaker Unified Studio projects. This feature addresses the deprecation of AWS CodeCommit while providing teams with a straightforward and consistent way to collaborate on project files across the integrated development tools in SageMaker.
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Accelerate your data and AI workflows by connecting to Amazon SageMaker Unified Studio from Visual Studio Code | Amazon Web Services

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By Lauren Mullennex, Anchit Gupta, Anagha Barve, Bhargava Varadharajan
| Amazon Web Services
AWS Big Data Blog Developers and machine learning (ML) engineers can now connect directly to Amazon SageMaker Unified Studio from their local Visual Studio Code (VS Code) editor. With this capability, you can maintain your existing development workflows and personalized integrated development environment (IDE) configurations while accessing Amazon Web Services (AWS) analytics and artificial intelligence and machine learning (AI/ML) services in a unified data and AI development environment.
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