Remove machine-learning-in-production-software-architecture
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Generative AI: A Self-Study Roadmap

KDnuggets

What started with curiosity about GPT-3 has evolved into a business necessity, with companies across industries racing to integrate text generation, image creation, and code synthesis into their products and workflows. For developers and data practitioners, this shift presents both opportunity and challenge.

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10 GitHub Repositories for Mastering Agents and MCPs

KDnuggets

By Abid Ali Awan , KDnuggets Assistant Editor on July 7, 2025 in Language Models Image by Author | ChatGPT Introduction AI agents are autonomous software entities that perceive their environment, make decisions, and take actions to achieve specific goals. 10 GitHub Repositories for Mastering Agents and MCPs 1.

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Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job

AWS Big Data

Embracing data as a product is the key to address these challenges and foster a data-driven culture. However, enterprises often encounter challenges with data silos, insufficient access controls, poor governance, and quality issues. In this context, the adoption of data lakes and the data mesh framework emerges as a powerful approach.

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Author visual ETL flows on Amazon SageMaker Unified Studio (preview)

AWS Big Data

Choose the Amazon S3 source node and enter the following values: S3 URI : s3://aws-blogs-artifacts-public/artifacts/BDB-4798/data/venue.csv Format : CSV Delimiter : , Multiline : Enabled Header : Disabled Leave the rest as default. Use case walkthrough In this example, we use Amazon SageMaker Unified Studio to develop a visual ETL flow.

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Optimizing vector search using Amazon S3 Vectors and Amazon OpenSearch Service

AWS Big Data

You’ll learn how to use the new S3 Vectors engine type in OpenSearch Service managed clusters for cost-optimized vector storage and how to use one-click export from S3 Vectors to OpenSearch Serverless collections for high-performance scenarios requiring sustained queries with latency as low as 10ms.

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

They’re taking data they’ve historically used for analytics or business reporting and putting it to work in machine learning (ML) models and AI-powered applications. They aren’t using analytics and AI tools in isolation. The next generation of SageMaker is set to do just that.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

Within seconds of transactional data being written into Amazon Aurora (a fully managed modern relational database service offering performance and high availability at scale), the data is seamlessly made available in Amazon Redshift for analytics and machine learning. Create dbt models in dbt Cloud. Prerequisites A dbt Cloud account.