Workflow Authoring Tool for Cloud-Agnostic Multi-Cluster MLOps
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Solution Overview
Problem
Existing MLOps solutions lack cloud agnosticism, pluggability, and the ability to orchestrate workflows across multiple clusters, leading to inefficiencies in ML model deployment and management, particularly in scenarios involving on-premise and edge deployments.
Innovation Solution
A Workflow Authoring and Realization Tool that generates cloud-agnostic, pluggable, and distributed workflow files using a unified interface, supporting various ML components and enabling orchestration across multiple clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing MLOps solutions are used, then ML model deployment can be achieved, but cloud agnosticism and pluggability are lacking, limiting adaptability across different deployment architectures
Solution Approach 1:
The patent implements a unified workflow authoring interface that can generate deployment configurations for multiple cloud platforms and edge devices. The system uses a standardized workflow description language that abstracts away platform-specific details, allowing the same workflow to be deployed across different cloud providers (AWS, Azure, GCP) and on-premise environments without requiring separate tooling or extensive platform-specific knowledge.
Solution Approach 2:
The patent introduces a workflow authoring tool as an intermediary layer between the user and the diverse deployment targets. This tool translates high-level workflow definitions into platform-specific deployment configurations, shielding users from technology-specific complexities while maintaining the ability to deploy to various cloud and on-premise environments.
2Adaptability or versatility
If manual orchestration of workflows across multiple clusters is performed, then deployment flexibility is achieved, but development and deployment time increases significantly
Solution Approach 1:
The patent enables users to define workflows in advance using a standardized description language that captures the essential logic and dependencies. These pre-defined workflows can be stored and reused, allowing rapid deployment to multiple clusters without manual reconfiguration. The system automatically handles the orchestration logic, eliminating the need for time-consuming manual setup while maintaining distributed deployment capabilities.
Solution Approach 2:
The patent allows workflow definitions to be copied and reused across different clusters and environments. Once a workflow is defined and validated in one environment, it can be replicated to multiple clusters with minimal modification, significantly reducing development and deployment time while maintaining consistency across distributed systems.
3Adaptability or versatility
If existing MLOps solutions are used, then ML operations can be performed, but the ability to orchestrate workflows across multiple clusters is limited
Solution Approach 1:
The patent segments the deployment system into distinct modular components: a workflow authoring module, a workflow description language layer, a translation engine, and deployment agents. This segmentation allows the system to handle multi-cluster orchestration by breaking down complex distributed workflows into manageable, independently deployable units that can be orchestrated across clusters without overwhelming system complexity.
Data Source
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AI summary
The embodiments relate to a method comprising receiving as input a first file describing one or more workflows for a machine learning operation, a set of second files describing particular tasks for said one or more workflows as defined in the first file, and a Configuration File containing domain specific implementation information associated with descriptions in the first and set of second files; generating one or more workflow files according to the received input; and converting at least one of the tasks of the one or more generated workflows into equivalent deployable task files describing an implementation of a machine learning task. The embodiments also relate to technical equipment for implementing the method.