Unite.AI

NOVEMBER 2, 2023

While most books on Generative AI focus on the benefits of content generation, few delve into industrial applications, such as those in warehouses and collaborative robotics. Here, “The Definitive Guide to Generative AI for Industry ” truly shines.

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AI Weekly

AUGUST 20, 2024

Welcome Bridging AI, Vector Embeddings and the Data Lakehouse Innovative leaders such as NielsenIQ are increasingly turning to a data lakehouse approach to power their Generative AI initiatives amidst rising vector database costs. Powered by onehouse.ai Can't make it? Register anyway to receive the recording!

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AWS Machine Learning Blog

JUNE 18, 2024

This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. A human-in-the-loop mechanism safeguards data ingestion.

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AWS Machine Learning Blog

APRIL 7, 2025

Amazon Bedrock Knowledge Bases offers fully managed, end-to-end Retrieval Augmented Generation (RAG) workflows to create highly accurate, low-latency, secure, and custom generative AI applications by incorporating contextual information from your companys data sources. He studied computer science at UW Seattle.

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Metadata Data Ingestion Generative AI Natural Language Processing 111

AWS Machine Learning Blog

OCTOBER 2, 2024

Large enterprises are building strategies to harness the power of generative AI across their organizations. Managing bias, intellectual property, prompt safety, and data integrity are critical considerations when deploying generative AI solutions at scale.

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AWS Machine Learning Blog

OCTOBER 23, 2024

Generative AI is set to revolutionize user experiences over the next few years. A crucial step in that journey involves bringing in AI assistants that intelligently use tools to help customers navigate the digital landscape. In this post, we demonstrate how to deploy a contextual AI assistant.

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Towards AI

NOVEMBER 3, 2024

Author(s): Devi Originally published on Towards AI. Part 2 of a 2-part beginner series exploring fun generative AI use cases with Gemini to enhance your photography skills!

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AWS Machine Learning Blog

DECEMBER 11, 2024

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. This way, when a user asks a question of the tool, the answer will be generated using only information that the user is permitted to access.

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AWS Machine Learning Blog

NOVEMBER 26, 2023

Today, we are excited to announce three launches that will help you enhance personalized customer experiences using Amazon Personalize and generative AI. Generative AI is quickly transforming how enterprises do business. FOX Corporation (FOX) produces and distributes news, sports, and entertainment content. “We

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NVIDIA

MARCH 19, 2025

Powering Intelligent Content Creation Accelerated computing enables AI-driven workflows to process massive datasets in real time, unlocking faster rendering, simulation and content generation. Sixth-Generation NVIDIA NVDEC: Provides up to double H.264 264 decoding throughput and offers support for 4:2:2 H.264

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AWS Machine Learning Blog

AUGUST 9, 2024

Retrieval Augmented Generation (RAG) has emerged as a leading method for using the power of large language models (LLMs) to interact with documents in natural language. The first step is data ingestion, as shown in the following diagram. This structure can be used to optimize data ingestion. What is RAG?

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AWS Machine Learning Blog

OCTOBER 6, 2023

Generative AI developers can use frameworks like LangChain , which offers modules for integrating with LLMs and orchestration tools for task management and prompt engineering. For ingestion, data can be updated in an offline mode, whereas inference needs to happen in milliseconds.

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Marktechpost

JULY 2, 2024

It is a platform designed to ingest and parse a wide range of unstructured data types—such as documents, images, audio, video, and web content—and convert them into structured, actionable data. This structured data is optimized for Generative AI (GenAI) applications, making it easier to implement advanced AI models.

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Snorkel AI

DECEMBER 2, 2024

When combined with Snorkel Flow, it becomes a powerful enabler for enterprises seeking to harness the full potential of their proprietary data. What the Snorkel Flow + AWS integrations offer Streamlined data ingestion and management: With Snorkel Flow, organizations can easily access and manage unstructured data stored in Amazon S3.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

This deployment guide covers the steps to set up an Amazon Q solution that connects to Amazon Simple Storage Service (Amazon S3) and a web crawler data source, and integrates with AWS IAM Identity Center for authentication. It empowers employees to be more creative, data-driven, efficient, prepared, and productive.

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AWS Machine Learning Blog

APRIL 26, 2024

You can now interact with your documents in real time without prior data ingestion or database configuration. You don’t need to take any further data readiness steps before querying the data. Additionally, you would need to manage cleanup when the data was no longer required for a session or candidate.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Rockets legacy data science architecture is shown in the following diagram. The diagram depicts the flow; the key components are detailed below: Data Ingestion: Data is ingested into the system using Attunity data ingestion in Spark SQL.

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AWS Machine Learning Blog

JULY 11, 2024

In this blog post, we explore how Agents for Amazon Bedrock can be used to generate customized, organization standards-compliant IaC scripts directly from uploaded architecture diagrams. Select the KB and in the Data source section, choose Sync to begin data ingestion. Double-check all entered information for accuracy.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

Retrieval Augmented Generation RAG is an approach to natural language generation that incorporates information retrieval into the generation process. RAG architecture involves two key workflows: data preprocessing through ingestion, and text generation using enhanced context.

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AWS Machine Learning Blog

AUGUST 5, 2024

Choose Sync to initiate the data ingestion job. After data synchronization is complete, select the desired FM to use for retrieval and generation (it requires model access to be granted to this FM in Amazon Bedrock before using). He specializes in generative AI, machine learning, and system design.

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AWS Machine Learning Blog

FEBRUARY 5, 2025

By fusing generative AI capabilities with intelligent information retrieval from your enterprise systems, Amazon Q Business delivers precise, context-aware responses firmly rooted in your organizations specific data and documents, enhancing its relevance and accuracy.

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AWS Machine Learning Blog

AUGUST 28, 2024

Choose Sync to initiate the data ingestion job. After the data ingestion job is complete, choose the desired FM to use for retrieval and generation. About the Authors Sandeep Singh is a Senior Generative AI Data Scientist at Amazon Web Services, helping businesses innovate with generative AI.

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IBM Journey to AI blog

OCTOBER 30, 2023

However, these were often stand-alone solutions that didn’t address the underlying issues of siloed, incomplete or duplicative data. Canada used integrated public health information systems, like Panorama, for seamless data ingestion, cleansing and import processing.

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AWS Machine Learning Blog

MAY 7, 2024

If you prefer to generate post call recording summaries with Amazon Bedrock rather than Amazon SageMaker, checkout this Bedrock sample solution. The service allows for simple audio data ingestion, easy-to-read transcript creation, and accuracy improvement through custom vocabularies.

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Marktechpost

SEPTEMBER 26, 2023

They also plan on incorporating offline LLMs as they can process sensitive or confidential information without the need to transmit data over the internet. This will reduce the risk of data breaches and unauthorized access. Check out the GitHub and Documentation.

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John Snow Labs

FEBRUARY 18, 2025

This talk will explore a new capability that transforms diverse clinical data (EHR, FHIR, notes, and PDFs) into a unified patient timeline, enabling natural language question answering.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) have taken the field of AI by storm. Scale and accelerate the impact of AI There are several steps to building and deploying a foundational model (FM). brings new generative AI capabilities—powered by FMs and traditional machine learning (ML)—into a powerful studio spanning the AI lifecycle.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

The teams built a new data ingestion mechanism, allowing the CTR files to be jointly delivered with the audio file to an S3 bucket. In the future, Principal plans to continue expanding postprocessing capabilities with additional data aggregation, analytics, and natural language generation (NLG) models for text summarization.

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ODSC - Open Data Science

MARCH 27, 2023

In this session, you will explore the flow of Imperva’s botnet detection, including data extraction, feature selection, clustering, validation, and fine-tuning, as well as the organization’s method for measuring the results of unsupervised learning problems using a query engine.

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Unite.AI

JUNE 4, 2024

Lastly, the integration of generative AI is set to revolutionize business operations across various industries. Google Cloud’s AI and machine learning services, including the new generative AI models, empower businesses to harness advanced analytics, automate complex processes, and enhance customer experiences.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Effectively manage your data and its lifecycle Data plays a key role throughout your IDP solution. Starting with the initial data ingestion, data is pushed through various stages of processing, and finally returned as output to end-users. Amazon Textract requires at least 150 DPI.

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AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications.

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Marktechpost

DECEMBER 3, 2024

As generative AI continues to grow, the need for an efficient, automated solution to transform various data types into an LLM-ready format has become even more apparent. Meet MegaParse : an open-source tool for parsing various types of documents for LLM ingestion. Check out the GitHub Page.

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AWS Machine Learning Blog

JULY 17, 2024

Amazon Q Business is a fully managed, secure, generative-AI powered enterprise chat assistant that enables natural language interactions with your organization’s data. Technical Account Manager specializing in generative AI solutions and dedicated to helping customers successfully adopt AI/ML on AWS.

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John Snow Labs

FEBRUARY 1, 2025

Additionally, I will discuss the hurdles faced, from ensuring accuracy in AI predictions to integrating machine learning with clinical workflows. This presentation demonstrates how bridging complexity and innovation can transform patient care and expand the possibilities of AI in healthcare.

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AWS Machine Learning Blog

MAY 25, 2023

One of the most common applications of generative AI and large language models (LLMs) in an enterprise environment is answering questions based on the enterprise’s knowledge corpus. Amazon Lex provides the framework for building AI based chatbots. Amazon SageMaker Processing jobs for large scale data ingestion into OpenSearch.

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Snorkel AI

DECEMBER 2, 2024

When combined with Snorkel Flow, it becomes a powerful enabler for enterprises seeking to harness the full potential of their proprietary data. What the Snorkel Flow + AWS integrations offer Streamlined data ingestion and management: With Snorkel Flow, organizations can easily access and manage unstructured data stored in Amazon S3.

Data Ingestion 52

Data Ingestion Large Language Models LLM Machine Learning 52