Automated ML Model Lifecycle Management with Self-Service Annotation
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Solution Overview
Problem
Conventional machine learning technologies face challenges such as manual and time-consuming data annotation, requirement of previously annotated data, inefficiency in handling unstructured data, slow model training, and high hardware resource needs, especially with imbalanced data sets.
Innovation Solution
A method and system for rapidly building, managing, and sharing machine learning models by receiving unannotated data, requesting annotations, building models, deploying them, and incorporating production annotations to generate new models, while selecting between models based on annotated or new data, and reporting quality metrics like precision and recall.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual brute force annotation is used, then data can be annotated, but time and cost increase substantially
Solution Approach 1:
The system enables self-service annotation by allowing the machine learning model to generate its own training data through automated annotation of unstructured data. The model processes raw data, generates predictions, and uses these predictions to create annotated training examples, eliminating the need for manual annotation while reducing time and cost.
Solution Approach 2:
The system performs preliminary automated annotation on unstructured data before formal model training. By pre-processing the data and generating initial annotations, the system prepares the training data in advance, reducing the time required for manual annotation and accelerating the overall model development process.
2Productivity
If previously annotated data is required, then model training can begin, but the system cannot start when such data is absent
Solution Approach 1:
The system performs preliminary automated annotation on unstructured data to create the initial training dataset. By pre-processing the raw unstructured data and generating annotations automatically before model training begins, the system eliminates the dependency on pre-existing annotated data while maintaining rapid model development capability.
Solution Approach 2:
The system is self-sufficient in creating its own training data by automatically annotating unstructured data. This self-service capability allows the system to start model training immediately with raw unstructured data without requiring external annotated datasets, enhancing both productivity and adaptability.
3Measurement precision
If extensive knowledge about data preprocessing and feature extraction is required, then model accuracy can be improved, but ease of operation decreases
Solution Approach 1:
The system performs data preprocessing, feature extraction, and annotation generation automatically without requiring user expertise. The machine learning model handles these complex technical tasks autonomously, allowing users with minimal knowledge to operate the system while maintaining high model accuracy through sophisticated automated processing.
Solution Approach 2:
The system replaces manual mechanical processes of data preprocessing and feature extraction with automated computational algorithms. This substitution eliminates the need for users to manually perform complex data science tasks while maintaining or improving accuracy through consistent, scalable automated processing.
4Measurement precision
If models are trained with substantial hardware resources, then model performance can be improved, but hardware requirements and cost increase
Solution Approach 1:
The system uses partial action by training models incrementally with progressively larger datasets rather than requiring all data to be processed simultaneously. This approach allows model performance to improve over time with moderate hardware resources, avoiding the need for substantial upfront hardware investment while still achieving high final performance.
Solution Approach 2:
The system performs preliminary automated annotation and data processing to create compact, high-quality training datasets. By pre-processing data to extract only the most relevant information, the system reduces the volume of data requiring intensive computational processing, thereby lowering hardware requirements while maintaining model performance.
5Measurement precision
If manual sequential annotation is used, then data can be processed, but productivity decreases
Solution Approach 1:
The system enables self-service annotation where the machine learning model automatically annotates unstructured data at scale. This automated approach maintains annotation quality through consistent application of the model's learned patterns while achieving high productivity by processing vast amounts of data in parallel without manual intervention.
Solution Approach 2:
The system replaces the manual mechanical process of sequential annotation with automated computational processing. This substitution enables parallel processing of multiple data points simultaneously, dramatically increasing annotation throughput while maintaining quality through the model's consistent application of learned annotation criteria.
Data Source
AI summary
In some aspects, systems and methods for rapidly building, managing, and sharing machine learning models are provided. Managing the lifecycle of machine learning models can include: receiving a set of unannotated data; requesting annotations of samples of the unannotated data to produce an annotated set of data; building a machine learning model based on the annotated set of data; deploying the machine learning model to a client system, wherein production annotations are generated; collecting the generated production annotations and generating a new machine learning model incorporating the production annotations; and selecting one of the machine learning model built based on the annotated set of data or the new machine learning model.