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Four steps to integrate AI within existing engineering systems
Reading time: 6 minutes
Building AI applications isn’t just modeling, but rather a complete set of steps that includes data preparation, modeling, simulation, testing and deployment. With the right tools and support, engineers and scientists can achieve success without having to become data AI experts.
With the increased availability of ‘big industrial data’, compute power and scalable software tools, it becomes easier than ever to use artificial intelligence (AI) in engineering applications. AI methods ‘learn’ information directly from data without relying on a predetermined equation as a model. They’re particularly suitable for today’s complex systems.
In the context of AI, engineers and scientists are improving technologies, driven by analytics based on industrial data. Analytics modeling is the ability to describe and predict a system’s behavior from historical data using domain-specific techniques for data preparation, feature engineering and AI models that can be trained with machine and/or deep learning. Combining these capabilities with automatic code generation, targeting edge-to-cloud, enables reuse while automating actions and decisions.
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