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Prescriptive AI: The Smart Decision-Maker for Healthcare, Logistics, and Beyond

Unite.AI

This capability is essential for fast-paced industries, helping businesses make quick, data-driven decisions, often with automation. By using structured, unstructured , and real-time data, prescriptive AI enables smarter, more proactive decision-making. Each plays a unique role in delivering accurate and context-aware insights.

Algorithm 276
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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

Flipboard

Amazon Q Business , a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprises systems. Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management.

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Most Frequently Asked Azure Data Factory Interview Questions

Analytics Vidhya

Introduction Azure data factory (ADF) is a cloud-based data ingestion and ETL (Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation.

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Re-evaluating data management in the generative AI age

IBM Journey to AI blog

It is no longer sufficient to control data by restricting access to it, and we should also track the use cases for which data is accessed and applied within analytical and operational solutions. Moreover, data is often an afterthought in the design and deployment of gen AI solutions, leading to inefficiencies and inconsistencies.

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Drasi by Microsoft: A New Approach to Tracking Rapid Data Changes

Unite.AI

Drasi’s machine learning capabilities help it integrate smoothly with various data sources, including IoT devices, databases, social media, and cloud services. This broad compatibility provides a complete view of data, helping companies identify patterns, detect anomalies , and automate responses effectively.

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What is Data Ingestion? Understanding the Basics

Pickl AI

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. This is where data ingestion comes in.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools. Rockets legacy data science architecture is shown in the following diagram.