Data Engineering System for Scalable Machine Learning
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Solution Overview
Problem
Existing data engineering systems for machine learning struggle to scale with large datasets and model sizes, leading to inefficiencies and the need for custom, time-consuming implementation-specific code that is difficult to maintain and reproduce.
Innovation Solution
A distributed data engineering system with an ingest processing module, record store, and transformation module that provides a standardized interface for online data transformation, enabling scalable and reproducible machine learning systems by discovering new features, collecting statistics, and transforming low-dimensional data into high-dimensional representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing data engineering systems are used for machine learning, then basic data transformation can be performed, but they fail to scale with large datasets and model sizes, resulting in slow or failed execution
Solution Approach 1:
The system segments the data transformation process into distributed microservices that can independently process different portions of large datasets. This segmentation allows the system to handle large data volumes by dividing them into manageable chunks processed in parallel across multiple nodes, directly addressing the scaling limitation of existing systems.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing a standardized interface layer that abstracts the complexity of data transformation. This interface dimension allows the system to scale horizontally by adding more processing nodes without increasing the complexity of individual transformation operations, enabling the system to handle both large data sizes and complex model sizes simultaneously.
2Adaptability or versatility
If implementation specific code is written to glue together machine learning software tools, then large-scale data engineering can be supported, but the system becomes time-consuming to create, brittle to maintain, and difficult to reproduce
Solution Approach 1:
The system implements a universal standardized interface that can work with multiple machine learning software tools and data sources. This interface serves multiple functions: data ingestion, transformation, and integration with various ML tools. By making the interface universal rather than tool-specific, the system achieves adaptability for large-scale data engineering while reducing complexity through standardization, eliminating the need for custom glue code for each tool combination.
Solution Approach 2:
The standardized interface acts as an intermediary layer between raw data sources and machine learning tools. This mediator abstracts the complexity of data engineering operations, providing a consistent API that works across different tools and platforms. The intermediary handles the adaptability requirements while shielding users from the underlying complexity, making the system both versatile and maintainable.
3Ease of operation
If a standardized interface for data transformation is implemented, then transformations become independent of programming languages and integrate with training systems, but this requires a modular framework that increases initial system complexity
Solution Approach 1:
The standardized interface is designed to be universally applicable across different programming languages and machine learning tools. It provides language-independent data transformation capabilities through a consistent API that can be implemented in various languages. The interface handles multiple functions including data validation, transformation, and integration with training systems, achieving ease of operation through universality while managing framework complexity through careful architectural design.
Data Source
AI summary
Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation. One embodiment provides a method for data engineering for machine learning at scale, the method including calling a built-in feature transformation or defining a new transformation, specifying a data source and compressing and storing the data, providing ingest-time processing by automatically analyzing necessary statistics for features, and then generating a schema for a dataset for subsequent data engineering. Other embodiments are disclosed herein.