AWS Machine Learning Blog

DECEMBER 4, 2024

Through simple conversations, business teams can use the chat agent to extract valuable insights from both structured and unstructured data sources without writing code or managing complex data pipelines. The structured dataset includes order information for products spanning from 2010 to 2017.

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AWS Machine Learning Blog

JUNE 18, 2024

With AWS Glue custom connectors, it’s effortless to transfer data between Amazon S3 and other applications. Additionally, this is a no-code experience for Afri-SET’s software engineer to effortlessly build their data pipelines. Her current areas of interest include federated learning, distributed training, and generative AI.

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AWS Machine Learning Blog

OCTOBER 23, 2024

Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning. He currently is working on Generative AI for data integration.

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Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning.

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AWS Machine Learning Blog

DECEMBER 6, 2023

Overview of RAG RAG solutions are inspired by representation learning and semantic search ideas that have been gradually adopted in ranking problems (for example, recommendation and search) and natural language processing (NLP) tasks since 2010. In practice, we implemented this solution as outlined in the following detailed architecture.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

This makes GPUs well suited for data-heavy, matrix math-based, ML training workloads, and real-time inference workloads needing synchronicity at scale. Both use cases require the ability to move data around the chip quickly and controllably. An important part of the data pipeline is the production of features, both online and offline.

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