Auto-ML Classification Data Readiness Check
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Solution Overview
Problem
Existing machine-learning systems face challenges in efficiently generating accurate models due to the need for large, well-labeled datasets, which can be costly and time-consuming to assemble.
Innovation Solution
A method is introduced that improves the usability and transparency of machine-learning applications by performing a data readiness check, retrieving a configuration file specifying keys for model generation, training machine-learned models based on these keys, and selecting the best model through dry runs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large, well-labeled datasets are assembled to train machine-learning systems, then model accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The system performs preliminary actions by automatically preparing and curating training datasets before the machine learning training process begins. This includes data collection, cleaning, labeling, and validation steps that are executed automatically, reducing the manual time investment required while ensuring high data quality for accurate model training.
Solution Approach 2:
The machine learning system performs self-service by automatically managing its own training data requirements. The system autonomously identifies, collects, and prepares appropriate training datasets without requiring extensive manual intervention, thereby reducing both time and resource costs while maintaining the quality necessary for accurate predictions.
2Measurement precision
If large, well-labeled datasets are assembled to train machine-learning systems, then model accuracy is improved, but resource costs increase
Solution Approach 1:
The system autonomously manages data collection and preparation resources, efficiently allocating computational and storage resources only when needed for specific training tasks. This self-service approach reduces overall resource consumption by eliminating wasteful manual processes and optimizing resource utilization throughout the data pipeline.
Solution Approach 2:
The system dynamically adjusts data processing parameters such as sampling rates, validation thresholds, and feature selection criteria based on the specific training requirements. This allows the system to achieve high model accuracy while consuming fewer resources by adapting the data preparation process to match the actual needs of each machine learning model.
3Adaptability or versatility
If machine-learning models are trained manually without automated selection, then model generation flexibility is maintained, but productivity decreases
Solution Approach 1:
The system implements automated feedback loops that evaluate model performance and automatically select or adjust training parameters based on results. This feedback mechanism maintains flexibility by adapting to different model requirements while significantly increasing productivity through automated iteration and selection processes that would be too time-consuming to perform manually.
Solution Approach 2:
The machine learning pipeline employs dynamic automation where the degree of automated model selection and data preparation adjusts based on the specific task requirements. This dynamic approach allows the system to maintain flexibility for complex, novel problems while automatically optimizing routine tasks, thereby increasing overall productivity without sacrificing adaptability.
Data Source
AI summary
A method of improving usability and transparency of machine-learning aspects of applications providing various types of services is disclosed. Based on a request submitted through an administrative user interface, a data readiness check is performed on underlying data associated with the application. Based on a successful completion of the data readiness check, a configuration file is retrieved from an application server. The configuration file specifies a plurality of keys for generating a machine-learned model for the application. The machine-learned model is trained based on the plurality of keys specified in the configuration file. The machine-learned model is selected from a plurality of machine-learned models based on dry runs of the each of the plurality of models. The machine-learned model is activated with respect to the application. Scores are identified from the underlying data items based on the selected machine-learned model.