You may be thinking that training and testing for AI systems is something you only do once. However, similar to using a defect management tool, testing and training have to be done continuously. If you care about the success of your AI systems, you need to constantly work on improving them.
So, sustained training and retesting cannot be ignored. But how do you do it? Let’s take a look at a few reasons why you need to do continuous retesting and training for your AI systems and how you can get it done.
Why Is Sustained Training and Retesting for AI Systems so Necessary?
When you’re just starting with test management software and AI systems, you might think that you only need to do this once. After that, the system will be good to go. But this can be a mistake, especially when you care about the quality of the projects you’re working on. Here are some reasons why you need to do continuous training and retesting for an AI system:
- By doing sustained testing, your AI system will keep growing and improving.
- You will make sure that every new piece of information is added to the system, so it adapts to it.
- Retesting ensures you eliminate potential issues that might arise along the way.
- With continuous training, your AI system will keep improving its understanding.
When Are Continuous Training and Retesting Necessary?
Retesting is usually required in the following situations:
- You need to do bug tracking to detect a possible issue and get rid of it
- The bug the tester issued was rejected by the developer
- The customers requested a retest
Conversely, here are some scenarios when continuous training is needed:
- You need to update the model
- You want to expand the system’s knowledge
- Certain data was updated, and the system needs to be aware of it
How Does Continuous Testing Work?
Here is how you can go through sustained testing with your defect management tool:
- First things first, you need to train the initial model. This is done with the help of a specific dataset that establishes the foundation of the abilities and knowledge of the AI model.
- Then, new data must be added as soon as it’s available. This keeps the system up-to-date and accurate.
- You must also update the AI system model based on the new data. This can include an incremental update or a complete model retraining process.
- Lastly, you must evaluate the new model’s performance, ensuring it works appropriately. Here, you can also make any potential improvements.
Final Thoughts
AI systems need to be maintained with sustained training and retesting. This makes sure that you keep defects away and that your model is accurate and reliable. With a few steps, such as training the initial model, adding new data, updating the system model, and evaluating the new model’s performance, you can make sure your AI system works as intended. This way, you’ll be able to gain a loyal fan base.
