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Alternatives to Azure Document Intelligence Studio: Exploring Powerful Document Analysis Tools

Seattle Data Guy

Document Intelligence Studio is a data extraction tool that can pull unstructured data from diverse documents, including invoices, contracts, bank statements, pay stubs, and health insurance cards. The cloud-based tool from Microsoft Azure comes with several prebuilt models designed to extract data from popular document types.

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Creating a bespoke LLM for AI-generated documentation

databricks

We recently announced our AI-generated documentation feature, which uses large language models (LLMs) to automatically generate documentation for tables and columns in Unity.

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Evaluating Methods for Calculating Document Similarity

KDnuggets

The blog covers methods for representing documents as vectors and computing similarity, such as Jaccard similarity, Euclidean distance, cosine similarity, and cosine similarity with TF-IDF, along with pre-processing steps for text data, such as tokenization, lowercasing, removing punctuation, removing stop words, and lemmatization.

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Streamline Operations and Empower Business Teams to Unlock Unstructured Data with Document AI 

Snowflake

It is estimated that between 80% and 90% of the world’s data is unstructured 1 , with text files and documents making up a significant portion. Every day, countless text-based documents, like contracts and insurance claims, are stored for safekeeping. Neither stage requires any ML- or application-development experience.

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Streamline RAG with New Document Preprocessing Features

Snowflake

As organizations increasingly seek to enhance decision-making and drive operational efficiencies by making knowledge in documents accessible via conversational applications, a RAG-based application framework has quickly become the most efficient and scalable approach. Until now, document preparation (e.g.

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Announcing Public Preview of AI Generated Documentation In Databricks Unity Catalog

databricks

Today, we are excited to announce the public preview of AI generated documentation in Databricks Unity Catalog. This feature leverages generative AI to.

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An educational side project

The Pragmatic Engineer

for the simulation engine Go on the backend PostgreSQL for the data layer React and TypeScript on the frontend Prometheus and Grafana for monitoring and observability And if you were wondering how all of this was built, Juraj documented his process in an incredible, 34-part blog series. Documenting the steps. You can read this here.

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