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The Role of Quality Assurance in Artificial Intelligence
In the contemporary age, Artificial Intelligence has become one of the most revolutionary technologies which has been deployed across numerous industries. Since, a lot of operations have started depending upon the power of artificial intelligence to be executed, deploying an efficient AI solution has become a challenge for businesses.
Like other business solutions, AI solutions must be examined and tested in order to unleash the best business insights and conclusions which help these organizations to scale better. Therefore, the role of Quality Assurance (QA) in AI can’t be underestimated to deploy AI models efficiently and accurately.
Throughout the AI model’s life cycle, QA checks must be performed on regular intervals to ensure successful deployment at any company or business. Since AI is a system which depends upon the fed data to improve itself continuously and consistently, quality assurance can’t be neglected at any cost.
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.
Quality Assurance and Training Data
Training Data is one of the most significant factors which goes a long way to ensure successful deployment of AI models in order to churn meaningful business insights. If we want our AI models to turn accurate, unbiased and reliable, then we must check the quality of training data.
For this, training data needs to be tested upon the parameters of bias, reliability, completeness and validity. In addition to this, the training data must be diverse enough to incorporate real world applications and insights.
Quality Assurance should not be taken just as a checklist item but seriously done at regular intervals so that our intelligent machines can yield the best and unbiased results which can be utilised by organisations to evolve their businesses.
How We Ensure Quality and Accuracy
At Automaton AI, we understand the significance of Quality Assurance comprehensively and perform periodic QA checks to ensure successful deployment of AI models throughout the five phases. From phase 1 ,i.e., taking extreme care of the data which is fed in the machine to phase 5 which involves retraining the model so that it keeps functioning at the optimal level, we believe in building the best and world class AI models. We equip our customers with powerful inhouse tools and assist them at all steps to provide the best AI-model experience in their respective organisations.