Machine Learning Workflow Diagram
A machine learning workflow diagram captures the end-to-end process of building a model, from problem definition to deployment and monitoring. It helps teams standardize their ML project process and onboard new data scientists. Tip: always include the evaluation feedback loop since iterating on features and models is a normal part of the workflow.
The prompt behind this diagram
Design a machine learning workflow diagram with stages: Problem Definition, Data Collection, Data Cleaning and Preprocessing, Feature Engineering, Train/Test Split, Model Selection, Model Training, Model Evaluation (metrics like accuracy and F1 score), a decision diamond for Performance Acceptable?, looping back to Feature Engineering if no, and proceeding to Model Deployment and Monitoring in Production if yes.
Paste your own description (or Terraform / docker-compose / SQL schema) into draft1 and get a diagram like this for your exact system.
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