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Designing generative AI workloads for resilience

AWS Machine Learning Blog

Data pipelines In cases where you need to provide contextual data to the foundation model using the RAG pattern, you need a data pipeline that can ingest the source data, convert it to embedding vectors, and store the embedding vectors in a vector database.

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6 benefits of data lineage for financial services

IBM Journey to AI blog

Increased data pipeline observability As discussed above, there are countless threats to your organization’s bottom line. That’s why data pipeline observability is so important. Trust and data governance Data governance isn’t new, especially in the financial world.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. The humble beginnings with Iris In 2017, SnapLogic unveiled Iris, an industry-first AI-powered integration assistant. He is an industry leader with a passion for innovation.

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Why We Started the Data Intelligence Project

Alation

Two data-driven careers. In 2013 I joined American Family Insurance as a metadata analyst. It takes numerous contributors working together and sharing knowledge in a centralized information space to avoid a complicated data ecosystem from collapsing. The first was in Madison, Wisconsin.

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Train An Emotion Recognition Model Using Multiple Datasets-Part 1

Mlearning.ai

FER, Facial Expression Recognition, is an open-source dataset released in 2013. Let’s take a moment to break down the project architecture shown above before we dive into the code. What is the FER dataset? It holds cropped facial images of size 48x48 pixels, represented in a flattened array of 2304 pixels.