Autonomous Benchmarking Service for ML Model Assessment

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Solution Overview

Problem

Benchmarking machine learning models is resource-intensive and requires significant user interaction, including knowledge of hardware and model specifics, making it time-consuming and difficult to manage effectively.

Innovation Solution

A benchmarking service that generates and updates execution plans autonomously based on user objectives, utilizing algorithms and training data to optimize resource usage, accuracy, and cost, while minimizing user input by batching similar jobs and utilizing available hardware resources efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional benchmarking methods are used, then measurement precision of model performance is improved, but device complexity and user burden increase significantly

Engineering Contradiction:

Improvemodel performance assessment accuracyVSAvoidbenchmarking process complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces a benchmarking service as an intermediary between users and the complex benchmarking infrastructure. This service automatically generates execution plans, manages resource allocation, and coordinates benchmarking jobs, thereby maintaining measurement precision while shielding users from system complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The benchmarking service implements self-service mechanisms by autonomously generating execution plans based on user objectives, automatically batching similar jobs, and dynamically allocating resources without requiring user intervention in the complex benchmarking process

Inventive Principle:

Principle #25Self-service

2Measurement precision

If comprehensive benchmarking is performed, then measurement precision is improved, but loss of time increases due to extensive user interaction requirements

Engineering Contradiction:

Improvemodel performance assessment accuracyVSAvoidbenchmarking setup and execution time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-generating execution plans based on user objectives before actual benchmarking begins. The benchmarking service prepares resource allocations, configures benchmarking parameters, and batches jobs in advance, eliminating time-consuming user setup interactions

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where the benchmarking service continuously monitors execution progress and automatically adjusts resource allocation based on real-time performance data, enabling comprehensive benchmarking without requiring user intervention during execution

Inventive Principle:

Principle #23Feedback

3Measurement precision

If resource-intensive benchmarking operations are executed, then measurement precision is improved, but use of energy and computational resources increases

Engineering Contradiction:

Improvemodel performance assessment accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The benchmarking service merges similar benchmarking jobs by batching multiple model evaluations together, consolidating resource usage. This approach maintains comprehensive measurement precision while reducing total energy consumption through efficient resource sharing and consolidated execution

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system dynamically changes execution parameters based on available resources and job priorities. The benchmarking service adjusts batch sizes, resource allocation, and execution timing to optimize the balance between measurement precision and energy consumption

Inventive Principle:

Principle #35Parameter changes

4Measurement precision

If detailed benchmarking control is provided, then measurement precision is improved, but ease of operation deteriorates due to user interaction requirements

Engineering Contradiction:

Improvemodel performance assessment accuracyVSAvoiduser interaction requirement

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The benchmarking service acts as an intermediary that translates simple user objectives into detailed execution plans. Users only need to specify high-level goals, while the service automatically handles complex parameter configuration, resource management, and execution control, maintaining precision while improving ease of operation

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10949252B1Benchmarking machine learning models via performance feedback

Publication Date: 2021.03.16 AMAZON TECH INC

AI summary

Techniques for benchmarking a machine learning model/algorithm are described. For example, in some instances a method includes generating an execution plan for benchmarking of at least one task corresponding to a machine learning model based on an identified machine learning model, identified training data, and at least one objective for the benchmarking job; receiving execution statistics about the execution of the task as a part of the benchmarking job according to the execution plan; and updating the execution plan based at least in part on the received execution statistics of the task.