How to Simplify Machine Learning Solutions for Renewable Energy and Utilities
Over the last few years, AI and machine learning technologies have been increasingly employed by utility-scale organizations in real-world applications. Whether used for load forecasting, yield optimization, predictive maintenance or demand management, utility companies are extracting tremendous value from their data. The reality is that implementing automated machine learning solutions can be quite costly and complex, with the need for collaboration across technologies, departments, and established processes.
In this webinar, gain a solid understanding of how to bring accessible machine learning capabilities to your organization at any point in the utility value chain - no coding necessary. A machine learning (ML) expert will talk about an intuitive and powerful AWS cloud-based solution that detects patterns, trends and correlations from data that hasn’t yet been fully exploited. Learn how to deploy predictive models using large, incomplete and noisy data around sensitive environmental factors, as will be demonstrated in wind and solar use cases. A case study will also be presented, demonstrating how the ML solution was used to build a powerful ML application for predicting and preventing contaminated water from reaching a wastewater treatment plant.
The future of renewable energy and utilities offers a world of opportunities for those who embrace AI and machine learning for a smarter, more efficient value chain. Join us and learn how.
Why choose AWS for your machine learning needs?
AWS enables organizations to undergo broad digital transformations with modern, cloud-native solutions. Offering a broad set of machine learning services and supporting cloud infrastructure, AWS enables organizations to tailor their machine learning solution to meet the unique needs of their business.