Model monitoring and maintenance is the final but continuous phase of ML development. As with all machines, a Vision AI must be regularly tuned and updated to meet performance expectations. If this essential step is not taken, the accuracy of the model can decrease over time.

Why do you need to maintain models?

The engineering team needs to recreate its model with many new data on fidget spinners, including providing the model with labelled images of fidget spinners and getting to know all keywords for fidget spinners. After this update, the performance of the model would theoretically rebalance to the desired threshold.

The fidget spinner example may illustrate how external conditions can change but emphasises the impact of changing Vision AI. Exterior changes are often more subtle and therefore harder to detect. However, the only way to ensure your model continues to perform as intended is by optimising your model regularly.

Advantages:

Time-based: Retrain your model regularly, regardless of how it works. A clear understanding is needed for time-based retraining of how frequently data and variables change in the environment of your model. If your intervals are too far apart, your model performance will decrease.

Continuous: Monitor key performance indicators (such as thresholds for precision and bias metrics) to determine the need for retraining. This is based on an extensive measurement panel that detects the potential of AI model management. Incorrect or vague measurements defeat the intention of the method.

Process:

In practice, what this means,

1. Invest in a team of MLOps. For one reason, this is an emerging field in AI development. MLS is what DevOps is for the development of software for AI. MLS is collaborative between data scientists and production teams, which differs from the usually siloed nature of data scientists (whose models are built) and engineers (who maintain it in production).

2. Make sure there are open lines of communication and collaboration between your data scientists and engineers if you are not ready for an MLS team. Data scientists have expertise in building and training the model to inform engineering decisions about model maintenance in production.

3. Incorporate customer feedback quickly. Whether it’s your MLOps team or your engineering team that manages the model in production, the units closest to the customer have to communicate (such as customer success). Customers can identify missing model errors or performance ranges; the more effective customer success can pass on this feedback to the manufacturing team, the faster the model can be retrained.

4. Get key buy-in stakeholders. You may not understand that building ML models aren’t a one-and-a-fits-all process. Education critical stakeholders (including executives) on the importance of ongoing model maintenance and re-training before launch to ensure sufficient time, money and staff are invested in the post-deployment phase.

5. Construct a pipeline for retraining. Make sure that you have the right tools and infrastructure to unlock efficiency before launching your AI model. Include people in the loop in your soil monitoring pipeline.

Conclusion: The AI model management is a generalised DSS architecture component for the creation, storage, handling, and access of models. There are several research opportunities to enhance its significant functions regarding artificial intelligence application.

Clare Louise