Multi-modal Machine Learning Model Training for Search
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Solution Overview
Problem
Conventional machine-learning model training techniques for service provider systems are inefficient and resource-intensive, taking hours or days to complete, which hinders real-time operation and requires significant time and resource commitment for training and retraining, limiting their use in time-sensitive scenarios.
Innovation Solution
The implementation of multi-modal machine-learning model training techniques, including a preview mode for real-time output and an expanded mode for increased accuracy, which allows for the generation and persistence of preview segments to improve operational efficiency and accuracy, enabling real-time output and refined segment definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine-learning model training techniques are used, then model training can be performed with traditional methods, but training time and resource consumption increase significantly
Solution Approach 1:
The patent segments the training data into multiple partitions and processes them in parallel across different computing devices. Each device trains on a subset of data simultaneously, then results are aggregated. This divides the monolithic training process into smaller concurrent tasks, reducing overall training time while maintaining model effectiveness.
Solution Approach 2:
The system performs preliminary actions by pre-processing and partitioning training data before the actual training begins. Data is segmented and prepared in advance, allowing parallel training processes to start immediately without sequential data processing delays, thus reducing total training time.
2Reliability
If conventional machine-learning model training techniques are used, then traditional training approaches can be maintained, but computational resource consumption increases
Solution Approach 1:
By segmenting the training workload across multiple computing devices, each device processes a smaller portion of data with reduced computational burden. The parallel processing architecture distributes resource consumption evenly, preventing any single device from being overwhelmed and reducing total energy consumption through efficient resource utilization.
Solution Approach 2:
The system uses partial action by processing data in manageable partitions rather than attempting to process all data simultaneously on a single device. This approach trains the model effectively on subset data while consuming fewer computational resources per device, with multiple partial results combining to achieve complete training objectives.
3Reliability
If conventional machine-learning model training techniques are used, then traditional training workflows can be followed, but real-time output is not achievable
Solution Approach 1:
The training process is segmented into parallel executable operations that can be performed simultaneously on different computing devices. Each device independently processes its data partition and generates training results in real-time, enabling the system to deliver immediate output while maintaining model accuracy through aggregation of parallel results.
Solution Approach 2:
The system implements dynamic parallel processing where multiple training operations execute concurrently and adaptively. Computing devices dynamically process data partitions in parallel, with results aggregated in real-time, enabling the system to provide immediate training outcomes while maintaining accuracy through coordinated multi-device execution.
Data Source
AI summary
Multi-modal machine-learning model training techniques for search are described that overcome conventional challenges and inefficiencies to support real time output, which is not possible in conventional training techniques. In one example, a search system is configured to support multi-modal machine-learning model training. This includes use of a preview mode and an expanded mode. In the preview mode, a preview segment is generated as part of real time training of a machine learning model. In the expanded mode, the preview segment is persisted as an expanded segment that is used to train and utilize an expanded machine-learning model as part of search.