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The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

In the generative AI or traditional AI development cycle, data ingestion serves as the entry point. Here, raw data that is tailored to a company’s requirements can be gathered, preprocessed, masked and transformed into a format suitable for LLMs or other models. One potential solution is to use remote runtime options like.

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Data4ML Preparation Guidelines (Beyond The Basics)

Towards AI

Table: Research Phase vs Production Phase Datasets The contrast highlights the “production data” we’ll call “data” in this post. Data is a key differentiator in ML projects (more on this in my blog post below). We don’t have better algorithms; we just have more data. It involves the following core operations: 1.

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Simplify automotive damage processing with Amazon Bedrock and vector databases

AWS Machine Learning Blog

This metadata includes details such as make, model, year, area of the damage, severity of the damage, parts replacement cost, and labor required to repair. The information contained in these datasets—the images and the corresponding metadata—is converted to numerical vectors using a process called multimodal embedding.

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How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning Blog

Deltek is continuously working on enhancing this solution to better align it with their specific requirements, such as supporting file formats beyond PDF and implementing more cost-effective approaches for their data ingestion pipeline. The first step is data ingestion, as shown in the following diagram. What is RAG?

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Drive hyper-personalized customer experiences with Amazon Personalize and generative AI

AWS Machine Learning Blog

You follow the same process of data ingestion, training, and creating a batch inference job as in the previous use case. Getting recommendations along with metadata makes it more convenient to provide additional context to LLMs. You can also use this for sequential chains.

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Data architecture strategy for data quality

IBM Journey to AI blog

Next generation of big data platforms and long running batch jobs operated by a central team of data engineers have often led to data lake swamps. Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving.

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Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Role of metadata while indexing data in vector databases Metadata plays a crucial role when loading documents into a vector data store in Amazon Bedrock. These identifiers can be used to uniquely reference and retrieve specific documents from the vector data store.