DataBreaths -The Institute of Data Science

In today's data-driven world, the ability to process and visualize streaming data in real-time has become increasingly valuable. This tutorial will guide you through creating a professional real-time dashboard using Python, demonstrating how to capture, process, and visualize streaming data as it arrives. Introduction to Real-Time Data Processing Unlike traditional batch processing where data is collected and analyzed periodically, real-time data processing allows you to analyze and visualize information as it's generated. This capability is essential for: Monitoring IoT sensors and devices Tracking financial market movements Analyzing user behavior on websites and applications Monitoring system performance metrics Detecting anomalies and responding to events instantly Architecture Overview Our real-time dashboard system consists of three main components: Data Producer : Simulates IoT sensor data and sends it to a message broker Message Broker : Handles data streamin...

Introduction The advent of large generative models, starting with GPT-3 in 2020, has revolutionized artificial intelligence. However, the significant computational resources required by these models have spurred a growing interest in sustainable AI. This analysis explores emerging approaches that move beyond the traditional "extractive" AI model towards cyclical, adaptive, and resource-conscious systems. We'll examine how concepts like continuous learning, resource optimization, and ecological principles can be integrated into AI infrastructure to minimize environmental impact and promote long-term viability. While the vision of fully "Regenerative AI Systems (RAIS)" remains largely theoretical, this analysis will focus on practical steps and research directions currently being pursued. The Challenge of Extractive AI Traditional AI development often follows a linear "extractive" model: Traditional AI Paradigm: [Data Extraction] → [Computation...

The Convergence Revolution: AI and Data Science in 2025 The landscape of artificial intelligence and data science has undergone a seismic shift in the past 18 months. What we're witnessing isn't merely an evolution but a convergence revolution—where previously distinct technological domains are colliding to create entirely new paradigms. In this inaugural post, we'll explore the technical underpinnings of these transformative trends and their far-reaching implications. 1. Foundation Models: Beyond Scale to Efficiency and Specialization While large language models have dominated headlines since 2022, the most significant development of 2024-2025 has been the pivot from scaling parameters to architectural efficiency and domain specialization. Technical Innovations in Model Architecture Recent progress in sparse mixture-of-experts (SMoE) architectures has dramatically changed the efficiency equation. The latest generation of models activates only a fraction of parame...