Blog Tags: MachineLearning
Analytics Leadership Transformation in Response to COVID-19: Key Strategies
Discover how analytics leaders are redefining their strategies in response to COVID-19. Understand the utilization of cloud technologies, proactive business collaboration, and how to adapt to new uncertainties based on the evolving role of data science.
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Magic off the pitch: Role of Data Analytics in Cricket
Author: Ramkumar Ananthakrishnan Data Analytics in cricket, especially in the immensely popular T20s, such as the Indian Premier League (IPL), […]
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How the Casual Restaurant Industry can Leverage Demand Forecasting to Up their Game
Author: Harish Seetamraju A Deep Dive into the Casual Restaurant Industry’s Forecasting Solutions, and an Investigation of What’s in Their Future. George […]
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Suez Canal Crisis & Building Resilient Supply Chains
The Suez Canal crisis was a catalyst for change in supply chain management. In this piece, we explore how leading companies are using AI, analytics, and digital twins to build more resilient, agile supply chains. Discover how proactive planning and smart technology can turn disruption into a competitive advantage.
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AI Can Revolutionize Industrial Safety; Save Lives
Author: Sri Vallabha Deevi Between May and July 2020, there were 30 recorded industrial accidents in India, killing at least […]
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Simplifying Geospatial Processing Using GeoPandas
Author: Karunakar Gumudala Introduction: Spatial datasets always are more than a few gigabytes in size, with millions of records […]
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Measuring Crop Health Using Deep Learning
Author: Ajith Raam D Agrochemical companies manufacture a range of offerings for yield maximization, pest resistance, hardiness, water quality, and […]
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How CPG Brands can leverage AI to fuel success on Amazon
Authors: Harini Shekar, Sunder Prabhu Pandemic Effect Recently, my 12-year-old had to write an essay as a part of his […]
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Maximizing Efficiency: Redefining Predictive Maintenance in Manufacturing with Digital Twins
Tiger Analytics leverages ML-powered digital twins for predictive maintenance in manufacturing. By integrating sensor data and other inputs, we enable anomaly detection, forecasting, and operational insights. Our modular approach ensures scalability and self-sustainability, yielding cost-effective and efficient solutions.
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