Cloud ML Notebook Feature Engineering Command Recommender
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Solution Overview
Problem
Machine learning notebook users face challenges in efficiently determining the next feature engineering steps during the feature engineering process, as existing tools lack effective recommendation systems that leverage previous commands and context to improve model development.
Innovation Solution
A system that operates within a cloud infrastructure, receives and stores previously executed machine learning notebook feature engineering commands, mines these commands to generate frequently used sets, and recommends next commands based on context and data, using algorithms like Apriori and Markov Chains to suggest optimal feature engineering steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If machine learning notebook users manually determine feature engineering steps, then they have full control over the process, but the time required to complete feature engineering increases significantly
Solution Approach 1:
The system enables feature engineering to be self-service by automatically analyzing previously executed commands and suggesting the next steps. The recommendation engine examines the current state of the notebook, identifies relevant features and transformations, and presents suggested commands that users can accept without manual analysis, allowing the system to serve itself in determining the feature engineering path.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring executed commands, storing them in a database, and using this information to generate personalized recommendations. The recommendation engine receives feedback about what commands have been executed and uses this to intelligently suggest the next appropriate commands, creating a closed-loop system that adapts to the user's specific workflow.
2Measurement precision
If the system stores and analyzes all previously executed commands, then recommendation accuracy improves, but system complexity and data storage requirements increase
Solution Approach 1:
The system extracts only the essential information needed for recommendations from the full command history. Instead of storing and analyzing every detail of all executed commands, the system extracts key features such as command types, parameters, and contextual metadata, storing only this summarized information in the database. This reduces storage requirements while maintaining recommendation accuracy.
Solution Approach 2:
The system segments the command analysis process into distinct modules: command storage, command mining, recommendation generation, and presentation. Each module handles specific aspects of the data processing pipeline independently, making the overall system more manageable and easier to maintain while enabling accurate recommendations through specialized processing at each stage.
3Manufacturing precision
If the recommendation system provides detailed context-aware suggestions, then the quality of feature engineering improves, but the computational resources required increase
Solution Approach 1:
The system applies partial action by providing recommendations only when and where they are most valuable. Rather than continuously analyzing all possible commands, the system focuses computational resources on analyzing commands relevant to the current notebook state and user workflow. The recommendation engine prioritizes generating suggestions based on the most recently executed commands and the current feature engineering stage, avoiding unnecessary computational analysis for irrelevant commands.
Data Source
AI summary
Embodiments operate a machine learning (“ML”) notebook in a cloud infrastructure executing a plurality of ML notebooks. Embodiments receive a plurality of previously executed ML notebook feature engineering commands from the plurality of ML notebooks. Embodiments store the plurality of previously executed ML notebook feature engineering commands, including a relationship between the feature engineering commands. Embodiments mine the stored commands to generate feature engineering sets of feature engineering commands, the feature engineering sets comprising feature engineering commands that are frequently used together and an order of use of the feature engineering commands. Embodiments then receive a context of a current feature engineering command and data used in the context and recommend a next feature engineering command to be executed after the current feature engineering command.