2 posts tagged with "Edge Computing"

Photo by Randy Fath on Unsplash

The journey of taking an open-source artificial intelligence (AI) model from a laboratory setting to real-world implementation can seem daunting. However, with the right understanding and approach, this transition becomes a manageable task.

This blog post aims to serve as a compass on this technical adventure. We'll demystify key concepts, and delve into practical steps for implementing anomaly detection models effectively in real-time scenarios.

Let's dive in and see how open-source models can be implemented in production, bridging the gap between research and practical applications.

Before we dive into the importance of data reduction for edge computing, it is important to define both terms. Data reduction refers to the process of reducing the amount of data that needs to be transmitted or stored, while still maintaining the necessary information and level of accuracy. This can be achieved through techniques such as compression, aggregation, and filtering.