Code-Free Automated Machine Learning Orchestrator
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Solution Overview
Problem
Organizations face challenges in implementing machine learning solutions due to the complexity of algorithms, the need for specialized expertise, and the time-consuming process of data preprocessing and model selection.
Innovation Solution
A code-free automated machine learning service that allows users to train high-quality custom machine learning models without writing code or requiring significant knowledge of machine learning concepts. The service uses a multi-tenant service provider network to deploy storage locations and a machine learning orchestrator that automates data preparation, feature engineering, algorithm selection, and hyperparameter optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning is implemented, then model development speed and accessibility are improved, but system complexity and computational resource requirements increase
Solution Approach 1:
An automated machine learning orchestrator is introduced as an intermediary component that manages the complex workflow between data sources, multiple ML algorithms, and deployment targets. The orchestrator abstracts the complexity by providing a unified interface and coordinating the automated processes, allowing users to benefit from automation without directly confronting the underlying system complexity.
Solution Approach 2:
The automated ML system is divided into distinct modular components including data preprocessing modules, multiple algorithm selection modules, hyperparameter optimization modules, and deployment modules. Each component handles a specific aspect of the ML pipeline independently, making the overall complex system manageable through clear segmentation and allowing parallel processing of different pipeline stages.
2Manufacturing precision
If multiple ML algorithms and pipelines are automatically explored, then model quality is improved, but computational time and resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing data and evaluating multiple ML algorithms in parallel before final model selection. The orchestrator proactively explores different algorithmic options and pipeline configurations in advance, using automated hyperparameter optimization and cross-validation to identify high-quality models before deployment, thereby reducing iterative experimentation time later.
Solution Approach 2:
The system implements partial action by exploring a curated subset of proven effective ML algorithms rather than exhaustively testing all possible algorithms. The automated pipeline focuses on evaluating a manageable number of high-potential models with optimized hyperparameters, achieving sufficient model quality without the excessive computational burden of complete algorithmic exploration.
3Ease of operation
If data preprocessing and feature engineering are automated, then ease of use is improved, but algorithmic complexity increases
Solution Approach 1:
The automated ML orchestrator implements self-service by automatically performing data preprocessing, cleaning, and feature engineering operations without requiring user intervention. The system autonomously detects data types, applies appropriate transformations, handles missing values, and generates relevant features based on the problem type, allowing users to simply provide raw data and receive ready-to-deploy models.
Solution Approach 2:
The system manages algorithmic complexity by dynamically adjusting preprocessing and feature engineering parameters based on the specific dataset and problem characteristics. The orchestrator automatically selects and configures appropriate preprocessing techniques and feature transformation parameters, abstracting the complex parameter tuning from the user while adapting to the specific requirements of each ML task.
Data Source
AI summary
Techniques for code-free automated machine learning (ML) are described. Users can train high-quality ML models and pipelines without necessarily needing to write code by providing a training dataset to a code-free machine learning service. The service may deploy an ML orchestration function and a storage location on behalf of a user. When a modification is made to the storage bucket, such as by the user providing a training dataset, the orchestration function is invoked and can automatically initiate an AutoML process using at least the training data to train multiple ML model variants. The resultant ML model(s) and associated metrics can be provided to the user, deployed behind an endpoint, and/or used to generate inferences.