How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model | Outerbounds

Amershi et al . [3] state that software teams “flight” changes or updates to ML models, often by testing them on a few cases prior to live deployment. Our work provides further context into the evaluation and deployment process for production ML pipelines: we found that several organizations, particularly those with many customers, employed a multi... See more

We found the ML engineering workflow to revolve around the following stages (Figure 1): (1) Data Preparation , which includes scheduled data acquisition, cleaning, labeling, and trans-formation, (2) Experimentation , which includes both data-driven and model-driven changes to increase overall ML performance, and is typically measured by metrics suc... See more