Well-Architected machine learning lifecycle
The ML lifecycle is the cyclic iterative process with instructions and best practices to use across defined phases while developing an ML workload. The ML lifecycle adds clarity and structure for making a machine learning project successful. The end-to-end machine learning lifecycle process illustrated in Figure 1 includes the following phases:
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Business goal identification
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ML problem framing
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Data processing (data collection, data preprocessing, feature engineering)
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Model development (training, tuning, evaluation)
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Model deployment (inference, prediction)
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Model monitoring
The phases of the ML lifecycle are not necessarily sequential in nature and can have feedback loops, a few of which are illustrated in Figure 1, to interrupt the cycle across the lifecycle phases.
Figure 1: ML lifecycle
The following is a quick introduction to each phase, which will be expanded upon later in this paper.
Business goal
An organization considering ML should have a clear idea of the problem, and the business value to be gained by solving that problem. You must be able to measure business value against specific business objectives and success criteria.
ML problem framing
In this phase, the business problem is framed as a machine learning problem: what is observed and what should be predicted (known as a label or target variable). Determining what to predict and how performance and error metrics must be optimized is a key step in this phase.
Data processing
Training an accurate ML model requires data processing to convert data into a usable format. Data processing steps include collecting data, preparing data, and feature engineering that is the process of creating, transforming, extracting, and selecting variables from data.
Model development
Model development consists of model building, training, tuning, and evaluation. Model building includes creating a CI/CD pipeline that automates the build, train and release to staging and production environments.
Deployment
After a model is trained, tuned, evaluated and validated, you can deploy the model into production. You can then make predictions and inferences against the model.
Monitoring
Model monitoring system ensures your model is maintaining a desired level of performance through early detection and mitigation.
The Well-Architected ML lifecycle, shown in Figure 2, takes the machine learning lifecycle just described, and applies the Well-Architected Framework pillars to each of the lifecycle phases.
Figure 2: Well-Architected ML lifecycle
