Low-Latency ML Prediction Cache for Network Distribution
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Solution Overview
Problem
Conventional systems for distributing and accessing machine learning model predictions across complex computer networks are inefficient due to high latency, inaccessibility, inaccuracy, and inflexibility, leading to time-consuming and resource-heavy processes for locating, storing, and utilizing predictions.
Innovation Solution
A low-latency machine learning model prediction cache is implemented, utilizing a prediction persistence layer and online datastore to cache current state predictions, allowing for efficient and flexible retrieval and distribution of predictions through a unified prediction persistence layer and prediction-fetching interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional systems utilize digital data repositories to store and distribute machine learning model predictions, then predictions can be stored and accessed, but the systems experience high latency and are time-consuming to locate, request, translate, and distribute predictions
Solution Approach 1:
The patent pre-generates and caches prediction results in a prediction cache before they are actually needed. The system performs prediction operations in advance, stores the results with associated metadata including model version information, and makes them immediately available for retrieval. This eliminates the need to execute predictions in real-time and reduces latency significantly.
Solution Approach 2:
The patent introduces a prediction service as an intermediary layer between the digital data repository and consuming systems. This service handles translation, version matching, and distribution of predictions, abstracting away the complexity from both the storage system and the consumers. The service resolves predictions to current model versions and manages the coordination between multiple components.
2Reliability
If conventional systems require detailed information about machine learning model versioning, pipelines, experimentation, context, implementation details, architectures, and languages, then comprehensive control and tracking are achieved, but identifying such information becomes time-consuming and resource-heavy
Solution Approach 1:
The patent extracts and caches essential prediction results and their associated metadata (including model version identifiers) directly from the complex machine learning pipelines into a simplified prediction cache. This separates the critical prediction information from the complex model training and versioning infrastructure, allowing quick retrieval without needing to access detailed implementation information.
Solution Approach 2:
The patent creates simplified copies of prediction results with embedded metadata that reference the original complex models. Instead of requiring consumers to access and interpret detailed model architectures, pipelines, and experimentation data, the system generates lightweight prediction objects that contain the essential information needed for accurate model-consumer matching.
3Adaptability or versatility
If conventional systems store predictions in various digital locations utilizing various different taxonomies, then comprehensive prediction storage is achieved, but the systems become inaccessible or undiscoverable to computing devices
Solution Approach 1:
The patent implements a universal prediction cache interface that can store and retrieve predictions from multiple different sources and taxonomies. The cache accepts predictions in various formats and from different model versions, normalizes them into a unified structure, and provides consistent access methods. This allows the system to maintain flexibility in how predictions are stored while providing ease of access through a standardized interface.
Solution Approach 2:
The prediction service acts as an intermediary that translates between various storage taxonomies and a unified access interface. It receives predictions from different locations and formats, resolves them to current model versions, and presents them through a consistent API, making the system both versatile in storage and easy to access.
4Productivity
If conventional systems provide machine learning model predictions from improper machine learning model versions, then predictions are available, but the accuracy of the requesting system is undermined
Solution Approach 1:
The patent implements a feedback mechanism where the prediction service continuously monitors model version information and updates the prediction cache accordingly. When new model versions are deployed or when predictions are generated, the service tracks the associated model versions and uses this information to resolve predictions to the correct current versions, ensuring accuracy while maintaining availability.
Solution Approach 2:
The system pre-resolves predictions to current model versions by caching predictions together with their associated model version metadata. Before predictions are consumed, the service performs version resolution to ensure that the latest accurate model versions are used, preventing the distribution of outdated or incorrect predictions.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a low-latency machine learning model prediction cache for improving distribution of current state machine learning predictions across computer networks. In particular, in one or more implementations, the disclosed systems utilize a prediction registration platform for defining prediction datatypes and corresponding machine learning model prediction templates. Moreover, in one or more embodiments, the disclosed systems generate a machine learning data repository that includes predictions generated from input features utilizing machine learning models. From this repository, the disclosed systems also generate a low-latency machine learning prediction cache by extracting current state machine learning model predictions according to the machine learning prediction templates and then utilize the low-latency machine learning prediction cache to respond to queries for machine learning model predictions.