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Machine learning benefits the environment & saves on construction costs

Managing Weather Damage w/Machine Learning to Benefit the Environment

Seasonal weather conditions put tremendous strain on critical infrastructure in several industries. Data collection is optimized with digital tools that make assets in oil, gas, water, wastewater, and energy run more efficiently, reducing environmental pollution in the long-term.

Real-time alerts sent to management quickly identify and address equipment repairs. Regulatory compliance becomes easier. The marriage of machine learning with renewable thermal energy reduces carbon emissions in the way that removing thousands of cars from the road benefits air quality. How does this work?


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The future of machine learning

What are the challenges to embed tinyML in edge devices?

Why use tinyML?

Most machine-learning (ML) focuses on high-powered solutions in the cloud or medium-powered solutions at the edge. However, there is another way to implement machine learning on small devices that have much less battery life. TinyML is intended for developing low-power machine learning models. It has wide-ranging applications in industries including security, healthcare, and smart-city technology. Its potential uses for critical infrastructure are in the research and development stage.

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Nature vs. Negligence:  Smart Infrastructure & Sustainability

Critical infrastructures operations significantly affect sustainability. The negative impact of critical infrastructure on the environment, society, and economy can exacerbate throughout their service life. It is crucial to maintain these impacts within desired limits. Increasingly stringent regulations make compliance more complicated. Where do infrastructure professionals begin? Let's start with a primer on sustainability and smart infrastructure.

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