  The traditional ML development methodology does not enable a large number of
contributors, each with distinct objectives, to work collectively on the
creation and extension of a shared intelligent system. Enabling such a
collaborative methodology can accelerate the rate of innovation, increase ML
technologies accessibility and enable the emergence of novel capabilities. We
believe that this novel methodology for ML development can be demonstrated
through a modularized representation of ML models and the definition of novel
abstractions allowing to implement and execute diverse methods for the
asynchronous use and extension of modular intelligent systems. We present a
multiagent framework for the collaborative and asynchronous extension of
dynamic large-scale multitask systems.
