ML Integrator Automating Model Deployment in Content Services
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Solution Overview
Problem
The integration of machine learning (ML) into content services platforms is complex and resource-intensive, requiring frequent updates and expert input, which leads to inefficiencies, security concerns, and difficulties in tracking historical configurations, making it costly and time-consuming.
Innovation Solution
An ML integrator that automatically sources, transforms, and deploys ML models, enabling dynamic model generation and utilization, with features like selective publishing, retraining with recent content, and auditing to reduce manual effort and enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are manually integrated into content services platforms, then model functionality can be implemented, but the integration process becomes complex and resource-intensive requiring frequent updates and expert input
Solution Approach 1:
The patent introduces an intermediary system that automatically manages the integration between machine learning models and content services platforms. This intermediary handles data transformation, model deployment, and version management, eliminating the need for manual expert intervention while reducing integration complexity.
Solution Approach 2:
The system enables self-service automation where the ML integration process automatically sources, transforms, and deploys models without requiring continuous expert input. The automated workflows handle retraining, version control, and deployment lifecycle management independently.
2Reliability
If frequent updates to ML models are performed, then model accuracy and relevance improve, but resource requirements and costs increase
Solution Approach 1:
The system implements periodic automated retraining of ML models using recent content from the platform. Instead of continuous updates, retraining occurs at scheduled intervals or triggered by specific conditions, balancing model freshness with resource conservation.
Solution Approach 2:
The system manages model versions by discarding outdated models and recovering computational resources. Old model versions are archived or deleted after serving their purpose, freeing up resources for training and deployment of newer versions.
3Loss of information
If manual tracking of historical configurations is performed, then configuration history can be maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system continuously and automatically logs all configuration changes, model versions, and deployment history. This continuous automated tracking eliminates manual intervention, ensuring complete historical records are maintained without consuming expert time.
Solution Approach 2:
An automated intermediary system manages configuration tracking and version control, serving as a mediator between model developers and the deployment environment. This intermediary automatically records all changes and maintains audit trails without requiring manual documentation.
4Productivity
If ML integration processes are simplified, then deployment speed increases, but security concerns may arise
Solution Approach 1:
The system implements automated feedback loops that verify security requirements during the deployment process. Security checks, validation rules, and compliance verification are automatically performed at each stage, ensuring security is maintained while enabling rapid automated deployment.
Data Source
AI summary
Various embodiments are generally directed to techniques for dynamically integrating ML functionality into computing systems, such as a content services platform (CSP), for instance. Many embodiments include ML integrated into a CSP and using production content as corpora (e.g., training and/or evaluation data). Some embodiments are particularly directed to generating and updating data for training and evaluating machine learning (ML) models, then making identified ML models available in various target environments. For example, embodiments may provide automatic, or semi-automatic, updating and deploying of ML models for making inferences, such as inferring labels for data in a content repository of a CSP.