Temporal Validity in Streaming ML Model Processing
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Solution Overview
Problem
In event stream processing, ensuring that data events are processed using the correct and temporally valid machine learning models is challenging, as models may change over time, leading to inconsistencies in predictive analysis results.
Innovation Solution
Implementing a system where streaming nodes receive data events with timestamps and utilize punctuation events to perform temporal join operations, ensuring that data events are only processed with temporally valid machine learning models by holding events until a valid model becomes available, and preloading new models to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are updated over time to improve predictive accuracy, then model effectiveness is improved, but temporal consistency of results deteriorates
Solution Approach 1:
The system performs preliminary actions by preloading machine learning models into memory before they are needed for processing. When a model is updated, the system proactively loads the new model version into memory in advance, ensuring that when data events arrive, the correct temporally valid model is already available, thus maintaining both accuracy and temporal consistency
Solution Approach 2:
The system introduces an intermediary component (model manager/memory) that mediates between model updates and data event processing. This intermediary manages model versions and their temporal validity, ensuring that data events are processed with the correct model version by checking timestamps and holding events if the appropriate model is not yet available
2Reliability
If data events are held until a temporally valid machine learning model is available, then model validity is improved, but processing time increases
Solution Approach 1:
By preloading models into memory before they are needed, the system eliminates the need to wait for model availability when data events arrive. The model is prepared in advance, so when the event needs processing, the valid model is already in memory, avoiding delays while ensuring validity
Solution Approach 2:
The system dynamically manages model loading based on actual needs. Instead of statically loading all possible models, it loads models into memory dynamically when punctuation events indicate they are needed, balancing memory usage with processing efficiency and minimizing delays
3Adaptability or versatility
If multiple copies of machine learning models are maintained for different event streams, then stream-specific processing is improved, but memory usage increases
Solution Approach 1:
The system implements universality by using a single copy of the machine learning model in memory that serves multiple event streams simultaneously. The model is not duplicated for each stream but is shared across all streams, reducing memory usage while maintaining the ability to process different streams with the same valid model
Solution Approach 2:
The system merges the model instances into a single shared copy in memory. Instead of having separate model copies for different event streams, it combines them into one universal model instance that is validated once and then applied to all streams, eliminating redundancy while preserving stream-specific processing capabilities
Data Source
AI summary
Data events of an event stream are processed in accordance with temporally valid machine learning models. A streaming node may receive data events via an event stream. Each data event may be associated with a timestamp. The streaming node may also utilize punctuation events that specify the temporal validity of available machine learning models. The streaming node performs a temporal join operation for each data event based on its timestamp and the temporal validity. If the data event's timestamp is less than or equal to the punctuation event's timestamp, the data event is provided to the temporally valid machine learning model for processing thereby. If the data event's timestamp is greater than the punctuation event's timestamp, the data event is held until a subsequent punctuation event specifying a later timestamp is received.