Techniques for adaptive pipelining composition for machine learning (ML)

This invention describes a system for automatically updating machine learning applications. It works by identifying a new machine learning model or new settings for an existing model, testing it offline, and if it performs better than the current version, automatically promoting it to be used in the live system. This update process can happen in a "shadow mode" first to ensure it meets certain conditions before going fully live.

Why it matters: Filed as MLOps was maturing. The explosion of large models since 2023 makes automated, adaptive deployment and monitoring critical for managing their complexity and performance.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, MLOps infrastructure

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