  The traditional Machine Learning (ML) methodology requires to fragment the
development and experimental process into disconnected iterations whose
feedback is used to guide design or tuning choices. This methodology has
multiple efficiency and scalability disadvantages, such as leading to spend
significant resources into the creation of multiple trial models that do not
contribute to the final solution.The presented work is based on the intuition
that defining ML models as modular and extensible artefacts allows to introduce
a novel ML development methodology enabling the integration of multiple design
and evaluation iterations into the continuous enrichment of a single unbounded
intelligent system. We define a novel method for the generation of dynamic
multitask ML models as a sequence of extensions and generalizations. We first
analyze the capabilities of the proposed method by using the standard ML
empirical evaluation methodology. Finally, we propose a novel continuous
development methodology that allows to dynamically extend a pre-existing
multitask large-scale ML system while analyzing the properties of the proposed
method extensions. This results in the generation of an ML model capable of
jointly solving 124 image classification tasks achieving state of the art
quality with improved size and compute cost.
