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ML-powered startups are revolutionizing the field of maintenance by leveraging the power of data analytics, sensors, and artificial intelligence to predict equipment failures, optimize maintenance schedules, and ultimately save significant time and resources. Predictive maintenance is the practice of using data, sensor technology, and advanced analytics to predict when a piece of equipment or machinery is likely to fail. ML-powered startups employ data integration platforms to gather and consolidate data from various sources, and use machine learning algorithms such as neural networks, decision trees, and random forests to analyze the data.