When you build an AI system, one of the most time-consuming tasks is the build of the data model and ensuring it is fit for the specific use case. Only when the model is developed, can it be deployed into production to perform its predictions on operational data.
A common issue is to find discrepancies between how the model performed, and how accurate it was, during build against the same observations of the model when it is given operational data.
Such discrepancies can occur because of the differences between the model’s build data and the operational data. Consequently, the model must be re-trained from time to time to keep improve its performance and keep it relevant. Doing so ensures that the model remains effective and that its predictions remain accurate in live situations.
The self-healing mechanism is a new standard for AI data model maintenance. This unique approach automates the prediction and re-training processes, streamlining the workflow and ensuring that your models are always up to date and performing optimally. The self-healing mechanism starts by pulling aggregated data from the database, which is then carefully divided into validation and prediction sets based on the availability of the target variable.
The mechanism then springs into action, comparing the validation set to the original training data used to build the model. It meticulously checks for changes in data volume and distribution using advanced statistical tests.
When a self-healing mechanism decides that the model needs retraining, it will automatically use a section of the validation set to re-train it. The remaining elements of the validation set it reserves to allow the updated model’s performance to be evaluated. This step is vital because it detects the drift of erroneous metrics, which ensures the model remains reliable and accurate, even as it ages.
When re-training is completed, a new updated model is integrated seamlessly into the process of prediction, which keeps your model in top condition.
Self-healing data models hold the potential to transform our digital world. The potential benefits, at a high-level, include:
A self-healing data model is capable of automatically identifying and correcting errors within its dataset. It means that they can evolve with new information or a change in the data environment without any intervention from a human, which can significantly reduce the resources and time needed to clean or preprocess data.
Unlike traditional AI data models that may degrade in performance as data quality diminishes or evolves, self-healing models improve over time. They learn from their interactions and corrections, continually enhancing their accuracy and reliability.
When statistical properties of explanatory variables change over time, this is known as data drift. It’s a challenge that often arises in AI applications. The difference with self-healing models is that they are built to detect and adjust around these changes on their own, which ensures that the AI application continues to be effective even when the underlying data changes.
The self-healing nature of these models minimizes the downtime associated with manual model maintenance and retraining. By autonomously correcting data issues, these models ensure that AI applications remain operational, providing continuous value to users and businesses.
Although the up-front development and implementation of self-healing data models requires notable investment, the ability of such models to maintain and improve themselves autonomously will lead to significant savings in the long-term. Self-healing models mean that manual labor is reduced, downtime is decreased and costly errors are avoided.
Self-healing data models are already transforming the resilience and efficiency of AI systems by automating the processes of prediction and retraining, ensuring models stay up-to-date and perform optimally.
These models address the common issue of discrepancies between the performance of AI models during development and their application in real-world scenarios. Through advanced statistical analyses, self-healing mechanisms automatically identify when a model needs retraining, using new data to maintain accuracy and reliability.
This autonomous error correction capability not only reduces the need for manual data cleaning but also enhances data quality over time. Furthermore, self-healing models adapt to changes in data environments, combating data drift and reducing model downtime. This innovative approach promises cost savings by minimizing manual maintenance and maximizing operational efficiency.
Implementing self-healing data models involves comprehensive system monitoring, automation through feedback loops, modular component design, and the use of chaos engineering to ensure robustness and resilience, setting a new standard for AI data model maintenance. By offering a solution that autonomously corrects and adapts to data changes, these models promise to make AI systems more resilient, efficient, and trustworthy.
