Digital transformation is driving companies to fundamentally rethink and modernize their business processes. In this context, AI-based workflow automation is gaining in importance, as it promises huge efficiency gains. However, AI-based workflows go one step further: They not only enable processes to be carried out more quickly and efficiently, but also enable data-driven analyses to make complex decisions in real time and to adaptively control processes.

In recent years, technological developments in artificial intelligence (AI) and machine learning have opened up many new possibilities. Two concepts that repeatedly emerge in this context are Large Language Models (LLMs) and Data Lakes. When used together, both technologies have the potential to significantly transform how companies handle data, analyze it, and extract knowledge.

Federated Learning offers an innovative solution by enabling collaborative learning from distributed data without centralization. This approach effectively enhances machine performance, preserves data privacy, and promotes efficient knowledge sharing among decentralized participants. In this article, we delve into the fundamentals of federated learning, its functionality, and real-world success stories.

To start and successfully run an IoT project, you need to be able to easily access and process information from many different devices, sensors, and machines in your IoT network. To do this, it is important that all these components can exchange data with each other in the same way: The OPC UA protocol provides the bridge to make this possible.

Industry 4.0 evolution depends on the transition to Edge AI, particularly following the era of cloud computing and the IoT. The adoption of Edge AI becomes the logical next step, as it holds the key to unlocking Industry 4.0's full potential.