Unified Dependency Graphs for ML Lifecycle Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity of managing and provisioning resources in large-scale distributed systems, such as data centers and cloud computing services, has increased due to the scale and scope of these systems, making it difficult to track, reproduce, and optimize the various stages of machine learning or software development lifecycles efficiently.
Innovation Solution
A unified paradigm for managing machine learning or software development models using dependency graphs, where transforms represent stages of the lifecycle, allowing for consistent tracking, reproducibility, and optimization, with transforms being deployed and reused across hosts, and versioning to manage data and code changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed systems scale up in size and scope to provide more computing resources and services, then the system's capacity and functionality improve, but the complexity of provisioning, administering, and managing resources increases
Solution Approach 1:
The patent introduces an intermediary system that sits between the distributed computing resources and the users/applications. This intermediary manages the complexity of resource provisioning, administration, and tracking by abstracting away the underlying system complexity, thereby maintaining adaptability and versatility while reducing management burden.
Solution Approach 2:
The patent creates a universal management framework that handles multiple functions (resource provisioning, administration, tracking, and optimization) through a unified system. This multi-functional approach reduces overall management complexity by consolidating various management tasks into a single versatile platform.
2Adaptability or versatility
If the machine learning or software development lifecycle includes multiple stages with various data and code transformations, then the model development capability improves, but the difficulty of tracking, reproducing, and optimizing each stage increases
Solution Approach 1:
The patent implements feedback mechanisms that automatically track and record transformations at each stage of the machine learning or software development lifecycle. This feedback system captures metadata about data and code transformations, enabling automatic reproduction and optimization while maintaining versatile model development capabilities.
Solution Approach 2:
The patent creates copies of transformation metadata and configuration information at each lifecycle stage. These copies enable automatic reproduction of experiments and optimizations without manually tracking each transformation, thereby reducing the difficulty of detecting and measuring lifecycle changes while preserving full model development capability.
3Productivity
If transforms are deployed across multiple hosts to optimize resource utilization, then the efficiency of computing resource use improves, but the complexity of managing and provisioning resources across hosts increases
Solution Approach 1:
The patent implements dynamic resource allocation and transformation deployment across multiple hosts. The system automatically adjusts transform deployment based on current resource availability and demand, optimizing productivity while reducing provisioning complexity through automated, adaptive management rather than static manual configuration.
Data Source
AI summary
Methods, systems, and computer-readable media for unified code and data management for machine learning models are disclosed. A plurality of dependency graphs, including a first dependency graph and a second dependency graph, are generated. The graphs comprise nodes associated with a software development model or machine learning model, and the nodes represent transforms. One or more transforms of the first dependency graph are used to generate first output corresponding to a node in the second dependency graph. One or more transforms of the second dependency graph are used to generate second output based at least in part on the first output.