Machine Learning Strategy Flowchart

Description

A structured flow diagram that visualizes the end-to-end machine learning workflow:

1. Data Sources – Unsupervised & Supervised data inputs (two dark-teal cards on left).
2. Feature Extraction – Key feature engineering step (bright-cyan card).
3. Model Training – Core ML algorithm processes features and produces either grouped objectives or predictive models (teal-green card).
4. Outputs & Feedback – Generates Group of Objectives and Predictive Model (dark-blue cards on right), which feed annotated data and new data back into the pipeline for continuous learning (bottom cards).

Arrows (solid for primary flow, dotted for feedback loops) clearly indicate data and control flow between stages. A concise header and sub-header explain the process context.

Who Is It For

• Data Science Teams mapping out their model development lifecycle.
• Machine Learning Engineers outlining architecture and data flows.
• Project Managers presenting ML project roadmaps to stakeholders.
• Consultants & Trainers educating clients on end-to-end ML pipelines.

Other Uses

• AI Solution Design diagrams for proof-of-concept or pilots.
• Data Ops & MLOps process documentation (add CI/CD loops).
• Academic Lectures illustrating core ML pipeline in courses.
• R&D Proposals showcasing iterative model improvement cycles.