Applied Machine Learning Prototypes for Hybrid Cloud Data Platform
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Solution Overview
Problem
The development and implementation of machine learning models and applications are complex and iterative, with existing tools not adequately supporting the entire machine learning lifecycle, leading to difficulties in designing, training, and deploying models effectively.
Innovation Solution
A data platform that enables the development, deployment, and monitoring of applied machine learning prototypes (AMPs), which provide end-to-end frameworks for building, deploying, and monitoring applications in near real-time, using a user-friendly interface and supporting the entire machine learning lifecycle with enhanced agility and collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are developed and implemented using existing tools, then model functionality and accuracy can be improved, but the complexity of the development process and difficulty of implementation increase significantly
Solution Approach 1:
The patent segments the machine learning development lifecycle into distinct phases (data preparation, model training, deployment, monitoring) and provides specialized tools for each phase. This modular approach reduces overall complexity by allowing users to focus on individual tasks rather than managing the entire complex workflow manually.
Solution Approach 2:
The patent creates a unified machine learning platform that performs multiple functions across the entire ML lifecycle - from data ingestion and preprocessing to model training, deployment, and monitoring. This multi-functional system reduces the need for multiple separate tools and simplifies the development process while maintaining model accuracy.
2Ease of operation
If comprehensive tools are provided to support the entire machine learning lifecycle, then ease of operation improves, but device complexity increases
Solution Approach 1:
The patent merges multiple previously separate tools and processes into a single integrated platform. By combining data preparation utilities, model training environments, deployment mechanisms, and monitoring systems into one cohesive system, the platform improves ease of operation through unified management while the internal complexity is abstracted away from users.
Solution Approach 2:
The patent introduces an intermediary layer that manages the complexity of the machine learning platform internally while presenting simplified interfaces to users. This mediator handles complex tasks such as resource allocation, model deployment coordination, and monitoring data processing, shielding users from underlying system complexity.
3Adaptability or versatility
If machine learning applications are developed from scratch, then customization and adaptability improve, but development time and loss of time increase
Solution Approach 1:
The patent provides pre-configured templates, pre-trained models, and pre-established pipelines for common machine learning tasks. These preliminary preparations allow users to quickly deploy customized solutions by modifying existing frameworks rather than building from scratch, significantly reducing development time while maintaining adaptability through configurable parameters.
Solution Approach 2:
The patent enables local customization within standardized frameworks, allowing users to modify specific components (such as data preprocessing steps, model parameters, or deployment configurations) without redesigning the entire system. This approach maintains adaptability for specific use cases while leveraging pre-built infrastructure to reduce overall development time.
Data Source
AI summary
Development of machine learning models and applications tends to be iterative and complex, made even harder because most of the necessary tools are not built for the entire machine learning lifecycle. Introduced here is a data platform that is able to accelerate time-to-value by enabling users to utilize applied machine learning prototypes (“AMPs”) made by others. These AMPs may be extendable, by the data platform, to new datasets, allowing machine learning to be developed and deployed more rapidly.