The five-phase lifecycle of building an AI model encompasses the following phases:
- Pilot Phase
- Data Annotation Phase
- Testing & Validation Phase
- Scaling Phase
- Retraining Phase
Building a world-class AI model in order to provide effective solutions to problem statements using data is a comprehensive process which can be distributed over five phases. QA checks and reviews must be done periodically to ensure successful deployment of AI models. Quality Assurance can be performed phase-wise in three ways in accordance to the current phase of the models.
Phase (1+2) : Pilot + Data Annotation Phase
The first step in the model building process involves identification of the problem statement. It involves gathering the required data which is needed to solve the problem. Quality Assurance performs qualitative testing of the data to be used for training the AI model in order to ensure the sufficient quality.
Phase (3+4) : Testing & Validation + Scaling Phase
During Phase 3 and 4, the AI model which was built in previous phases undergoes testing and validation process so that it can be scaled to a wider audience domain. Verification of the model at this stage using QA checks is quite significant as it will be going for a wider audience level .
Phase 5 : Retraining Phase
Retraining Phase plays a pivotal role in order to make any AI model a successful one. QA needs to be done so that the model continues to improve and learn consistency and deliver the accurate and qualitative results.