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Business Process Mapping basics
In this lesson
Business Process Mapping basics
Map an existing AI/ML workflow to its constituent phases, decision points, and handoffs using the standard lifecycle structure (business goal identification, problem framing, data processing, model development,…
You'll be able to
- Map an existing AI/ML workflow to its constituent phases, decision points, and handoffs using the standard lifecycle structure (business goal identification, problem framing, data processing, model development, deployment, and monitoring) documented in enterprise ML frameworks [^1][^2].
- Evaluate whether a business problem requires machine learning by comparing ML approaches against simpler alternatives and assessing cost, resource, and outcome trade-offs before committing to an ML solution path [^6].
- Identify feedback loops and iterative cycles within a process map that connect model performance evaluation back to data preparation and problem framing, ensuring the diagram reflects real-world ML workflows rather than purely sequential steps [^2][^7].
- Construct a process map that traces business objectives to measurable success criteria, aligning each lifecycle phase with key performance indicators and evaluation metrics that validate whether the ML initiative delivers the intended business value [^1][^5].
- Classify process components (such as feature stores, model registries, and performance feedback loops) within an ML lifecycle architecture diagram, distinguishing between online and offline data flows and between training and inference pathways [^7].