Notebook Interface for Enterprise ML Model Packaging
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Solution Overview
Problem
The process of converting a prototype machine learning (ML) model from a notebook application to an enterprise ML recipe is repetitive, cumbersome, and time-consuming, requiring substantial rewriting and packaging efforts by enterprise software teams.
Innovation Solution
A notebook interface with an embedded runtime environment and a packaging service that automates the creation and packaging of an enterprise ML recipe, allowing seamless transition from prototype to enterprise-ready model, enabling reuse across different contexts and use cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a prototype ML model is developed in a notebook application, then model experimentation and iteration are enabled, but the transition to enterprise platform requires repetitive rewriting and packaging
Solution Approach 1:
The patent creates a copy of the notebook environment within the enterprise platform through a runtime environment that replicates the notebook interface and execution capabilities. This allows the enterprise ML recipe to directly utilize the prototype model code without manual rewriting, as the runtime environment executes the original notebook code as if it were running locally.
Solution Approach 2:
The runtime environment acts as an intermediary layer between the notebook application and the enterprise platform. It translates and executes notebook code within the enterprise environment, enabling seamless integration without requiring code rewriting. The packaging service also serves as an intermediary that automatically packages the runtime environment and model code into a deployable ML recipe.
2Reliability
If enterprise software team rewrites code to create enterprise ML recipe, then model is suitable for enterprise platform, but process is cumbersome and time-consuming
Solution Approach 1:
The system enables self-service model deployment by allowing data scientists to create enterprise ML recipes directly from their notebook prototypes without requiring enterprise software team intervention. The packaging service automatically handles the complex tasks of environment packaging, dependency management, and recipe generation, making the process autonomous and eliminating manual rewriting.
Solution Approach 2:
The runtime environment is prepared in advance within the notebook application, creating a self-contained execution environment that includes all necessary dependencies and configurations. This preliminary setup allows the model to be deployed to the enterprise platform without requiring subsequent code modifications or environment reconfiguration.
3Ease of manufacture
If prototype model is passed to production team for rewriting, then enterprise ML recipe is generated, but resources and cost increase
Solution Approach 1:
Instead of manually copying and rewriting code, the system creates a virtual copy of the notebook runtime environment that executes the original prototype code. This eliminates the need for resource-intensive manual code rewriting and validation processes, reducing both time and computational resources required.
Solution Approach 2:
The packaging service automatically performs all necessary packaging, validation, and recipe generation tasks without requiring production team resources. The system self-manages the entire transition process from prototype to enterprise-ready model, eliminating the need for additional human resources and reducing operational costs.
Data Source
AI summary
Systems and methods for developing enterprise machine learning (ML) models within a notebook application are described. The system may include a notebook application, a packaging service, and an online ML platform. The method may include initiating a runtime environment within the notebook application, creating a plurality of files based on a notebook recipe template, generating a prototype model within the data science notebook application by accessing the plurality of files through the runtime environment, generating a production recipe including the runtime environment and the plurality of files, and publishing the production recipe to the online ML platform.