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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?

Spiraling energy costs and toxic carbon emissions are two factors driving demand for clean, efficient heating and cooling systems. Little-known outside the power generation industry, a technology called district energy is transforming offices and other public buildings.

Ayyeka's commitment to positive environmental impact is more than a "check the box" exercise. Following a careful analysis of the intersection between critical infrastructure and the United Nations Sustainable Environmental Goals, Ayyeka has chosen a specific focus on four goals:

This article was recently published by toolbox.com as https://www.toolbox.com/tech/artificial-intelligence/guest-article/game-changers-in-ai-and-infrastructure-are-reaching-market-maturity/#SocialMediaInterests

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.

A new field of Machine Learning called tinyML makes it possible to run a Machine Learning models on tiny, battery powered Internet of Things (IoT) devices. These devices will play a central role in helping organizations meet Environmental, Social, and Governance (ESG) goals.

With the ease in travel restrictions after two years, Ayyeka is back on the road visiting old friends in the U.S. and entertaining new ones at our Jerusalem HQ in Q1 2022.

Digitalization of data is a multi-step process. Managers who want to achieve the maximum benefit from data collection processes need to learn, embrace, and implement all stages of digitalization. Data should be considered not only integral to critical infrastructure, but as a form of infrastructure itself. (Part 3 of 3--Data Utilization)

Digitalization of data is a process involving several steps. Executives who want to achieve the maximum benefit from their data collection processes need to learn, embrace, and implement digitalization in all its stages. (Part 2 of 3--Data Collection)

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.