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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. ” Vitaly Tsivin, EVP Business Intelligence at AMC Networks. To bridge the tuning gap, watsonx.ai

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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning Blog

Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. In addition, the generative business intelligence (BI) capabilities of QuickSight allow you to ask questions about customer feedback using natural language, without the need to write SQL queries or learn a BI tool.

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A beginner tale of Data Science

Becoming Human

Just like this in Data Science we have Data Analysis , Business Intelligence , Databases , Machine Learning , Deep Learning , Computer Vision , NLP Models , Data Architecture , Cloud & many things, and the combination of these technologies is called Data Science. Data Science and AI are related?

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5 Key Components of Power BI: A Comprehensive Guide

Pickl AI

Summary: Power BI is a business intelligence tool that transforms raw data into actionable insights. Introduction Managing business and its key verticals can be challenging. Power BI is a powerful business intelligence tool that transforms raw data into actionable insights through interactive dashboards and reports.

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Top Data Analytics Trends Shaping 2025

Pickl AI

Automated Data Integration and ETL Tools The rise of no-code and low-code tools is transforming data integration and Extract, Transform, and Load (ETL) processes. It automates tasks like feature selection and model optimisation, enabling businesses to build robust models faster. and receiving instant, actionable insights.

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

Data warehouses were designed to support business intelligence activities, providing a centralized data source for reporting and analysis. This multidimensional analysis capability makes OLAP ideal for business intelligence applications, where users must analyze data from various perspectives.