NLU Model Hosting via Auto-Scaling and Consistent Hashing
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Solution Overview
Problem
Current systems face inefficiencies in hosting and managing large numbers of natural language understanding (NLU) models, particularly due to resource wastage when only a small fraction of models receive traffic, leading to suboptimal use of resources.
Innovation Solution
Implementing an auto-scaling, distributed, multi-tenant hosting system for machine learning models, where inference operations are routed through an inference service group with auto-scaling hosts, and using a consistent hashing scheme to optimize resource allocation and usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed number of NLU models are hosted to handle diverse intents, then the system can support various natural language understanding tasks, but resource wastage occurs when only a small fraction of models receive traffic
Solution Approach 1:
The system dynamically adjusts the number of hosted NLU models based on real-time traffic patterns and intent distribution. Instead of maintaining a fixed set of models, the system automatically scales the model inventory up when new intents are detected and down when traffic decreases, optimizing the balance between versatility and resource utilization.
Solution Approach 2:
The system changes the parameter of model inventory size based on observed traffic characteristics. By monitoring which models receive traffic and at what frequency, the system adjusts the total number of models hosted, transforming a static resource allocation into a dynamic parameter that adapts to actual usage patterns.
2Adaptability or versatility
If multiple NLU models are hosted to handle different intents, then the system can process various natural language tasks, but the complexity of managing and routing inference requests increases
Solution Approach 1:
The system introduces an intermediary routing layer that sits between the incoming inference requests and the NLU models. This intermediary component automatically determines which models should be hosted based on traffic analysis and manages the mapping between requests and models, abstracting away the complexity of direct model management.
Solution Approach 2:
The system implements feedback mechanisms that monitor model traffic patterns and use this information to automatically adjust the model inventory. This closed-loop feedback system reduces management complexity by automating decisions based on observed performance and usage patterns.
3Adaptability or versatility
If a large number of NLU models are hosted to ensure comprehensive intent coverage, then the system can handle more diverse queries, but the cost of maintaining and loading models increases
Solution Approach 1:
The system performs preliminary analysis of traffic patterns and intent distribution before fully committing to hosting specific models. By anticipating which models will be needed based on observed data, the system can proactively adjust the model inventory to match future demands rather than reacting to usage patterns after they occur.
Data Source
AI summary
Techniques for building slot types and slot type artifacts are described. For example, in some implementations, a natural language understanding (NLU) service is implemented by instructions that upon execution are to cause: receiving a request to build a slot type, the request including an indication of one or more locations storing information need to build a slot type, a slot type being a list of one or more values used to train a machine learning model to recognize a value for a slot; retrieving a slot type definition bundle associated with the request, the slot type definition bundle at least including a slot type definition defining resolution activities for the slot type; building the slot type using the retrieved slot type definition bundle; and storing into the storage slot type artifacts produced from building the slot type to be used during an analysis of an utterance.