An AI project is an organized effort to create and deploy an AI solution for a defined business objective. It begins by establishing why AI is preferable to existing processes and whether the expected benefits justify the resources. Successful work treats development as an iterative journey shaped by feedback, rather than a one-way sequence. section 10.5 section 10.6
A practical AI project lifecycle includes:
- Define the business outcome, constraints, and operating requirements. section 10.6
- Gather and label data, select representative samples, and check quality. section 10.5
- Enrich the data, build the model, and test its resilience, performance, and scalability. section 10.5
- Deploy the solution, then monitor and optimize it continuously. section 10.5
Start with a bounded business problem
A company might begin by using machine learning to investigate customer losses or identify products frequently purchased together. A modest, achievable objective creates an initial success and builds capability for larger projects. A credit-card authorization system illustrates the operational challenge: it must run continuously, respond within milliseconds, and process tens of thousands of transactions per second. section 10.6 section 10.6
Figure 10.2 An example of a flowchart that depicts the various steps to design and deploy an AI model
Use the flowchart to scan the main steps from designing through deploying an AI model. section 10.5
