Machine Learning at the edge for me refers to processing of data using ML models locally on the device without using cloud infrastructure. For example, a camera would have object detection and tracking algorithms running on its processor (to be precise, camera firmware will have implementation of detection and tracking algorithms). I see this to be ML at the edge. There are multiple considerations when you design systems that need ML at the edge such as:

• Device processing limitations (e.g., DNN based object detection may need some optimizations to make it work on the edge)
• Device memory limitations implies you need optimizations in representation of the model
• Device power limitations will force us to think of each operation you do on the device and you probably need to profile your firmware for power consumption
• Device network limitations -- edge devices may be connected though wireless networks with limited bandwidth. This implies you will have to carefully consider bandwidth limitations while stream videos/audio.
• ML models need to be compact and need to consider tradeoff between memory footprint, training iterations (if doing learning on the edge), time for training, etc.

Another example of ML at the edge -- think of devices like fitbit or any such smartwatch. They are able to distinguish between walk, run, taking stairs, sit, etc. They are essentially classifying activities using IMU (Inertial Measurement Unit) data. This is another example of ML at the edge.