Part 3/8:
Its introduction raises critical questions on the efficiency and capabilities compared to larger, established platforms. The moment Deep Seeq became open-source, it allowed users to run the models locally, presenting an enticing alternative to cloud-based solutions. However, that also brings along an essential element of privacy and data control.
The Case for Running Models Locally
One of the foremost arguments for running AI models like Deep Seeq locally revolves around data privacy. Utilizing cloud servers means your data is stored and owned by the provider; consequently, your inputs could be accessed or logged without your explicit consent.