Edge Computing And The Future Of Machine Learning Articles Big Data

There are, of course, limitations to what you can do at the edge. Today's machine learning algorithms are designed to run on powerful servers. Therefore, in the case of driverless cars, much of the heavy lifting still takes place in the cloud, with algorithms trained using millions of miles of recorded driving data before being deployed at the edge for inference. Increasingly, however, in other applications, we are starting to see algorithms trained locally too. This is far more cost-effective, requiring less ongoing bandwidth and storage cost. Swim, for example, is a streaming data analytics startup that uses a distributed network architecture to operate self-training machine learning at the edge in real-time.