Dynamic Model Versioning and Fork Management

AI models evolve constantly, and managing these versions on-chain can become complex. Nesa incorporates a sophisticated version control system for models that handle forks and merges in model evolution, akin to Git but for AI training processes. This system tracks and merges various AI training paths, facilitating more complex evolution, adaptations, and experimentations with model updates.

When developing AI, it is imperative to track different versions of your models and corresponding input data. This requires robust version control mechanisms for both models and datasets alike. By logging the lineage of data and model updates on Nesa, companies can provide version histories necessary for ensuring that the correct versions are always used or referenced, and that rejected model updates can be learned from and calibrated on.

AI Kernels on Nesa can automate staying in corporate compliance and general compliance. This level of container adaptability allows for the potential rollback of heterogeneous AI models in the event of an unsuccessful update, application downtime, DMCA takedown notice, user/customer complaint, or system exploit.

AI Kernels benefit from the unique Configuration Charter that they are instantiated with on Nesa, which sets a universal template for the easy replication of their specifications and procedures for such a Marketplace to exist.