Shared Pipeline Architecture for Transfer Learning

The patent merges previously independent training and deployment pipelines into a unified shared pipeline architecture. This allows models to be trained once and shared across multiple deployment pipelines, enabling transfer learning while maintaining automated continuous deployment. The shared pipeline stores trained models that can be reused by multiple downstream applications.

The shared pipeline architecture creates a universal model repository that serves multiple functions: training new models, storing pre-trained models for transfer learning, and providing models to multiple different deployment pipelines. This multi-functional system resolves the contradiction by making the deployment infrastructure adaptable to different use cases while remaining automated.

The patent segments the model lifecycle into distinct components: a shared training pipeline that produces general models, and multiple task-specific deployment pipelines that apply transfer learning. This segmentation allows each component to specialize - the shared pipeline optimizes for general patterns while deployment pipelines optimize for specific tasks, achieving both task-specific optimization and performance improvement through transfer.

The shared pipeline acts as an intermediary between data sources and multiple deployment pipelines. It receives training data, produces trained models, and distributes them to various deployment pipelines. This intermediary structure enables transfer learning by making trained models available as intermediates that can be adapted by multiple downstream tasks.

The shared pipeline is designed as a universal infrastructure that handles multiple functions: model training, model storage, and model distribution. By creating this universal component, the patent reduces overall system complexity despite enabling transfer learning across multiple pipelines. Instead of each pipeline needing its own training infrastructure, they all share the same universal system.

The patent uses copying of trained models from the shared pipeline to multiple deployment pipelines. Rather than maintaining complex interconnections between all pipelines, the system copies proven models and adapts them for specific tasks. This copying approach simplifies the architecture by decoupling the training complexity from deployment complexity.

The patent combines multiple manual training operations into a single automated shared pipeline. Instead of manually training separate models for each deployment task, the system automatically trains general models in the shared pipeline that can then be transferred and adapted. This merging maintains accuracy through proper transfer learning while dramatically improving deployment efficiency through automation.

The shared pipeline performs preliminary model training in advance, creating a repository of pre-trained models before they are needed for specific deployment tasks. This preliminary action allows deployment pipelines to start with pre-trained models rather than training from scratch, significantly reducing time and resource consumption while maintaining the ability to optimize for task-specific accuracy through transfer learning.