Troubleshooting Model Drift in Production AI System

As the world keeps changing, there is always a new form of data in a second. And we know that this world is of AI and What do the AI models work on? You guessed it, They work on data as they learn from it and make their prediction from it. When the AI models do not get the new data they will start to make mistakes and sometimes these mistakes may lead to a bigger problem. This issue is called as model-drift When the AI model is trained on the older data as the new data is not given. This leads to wrong decisions and business losses. What if, there is an AI system that detects fraud in online transactions? If the hackers start using some new tricks while the model is still working on the old fraud pattern, the AI might fail to catch the scammers which will lead to the loss of money. That is the reason we need to give some of the important AI models crucial and new data not every second but regularly each day to make these models more accurate.

For more information about the model drift visit – IBM

How to Detect Model Drift

To have more accurate data in the system, AI models need to be checked regularly to see if it is performing well, There are some ways we can find it:

1. Monitor the model’s Performance: It’s not like we need to check the model every second, we just need the model to check once a week (Depending upon how things are doing in the real world), to make sure that it is giving us the correct results. But what if the model starts to make mistakes in its every step? That means that the model is not working properly and needs to be replaced or there are some issues with its data.

2. Compare Old and New Data: We know a change in someone’s life is very crucial. Similarly, if we make the changes too much it will make some weird mistakes. That’s why we need to change the data to the familiar pattern. We should check on how the new data is different from the old one. But what if there is a big change? How will the model do?

Now if there is a big change in the data, the AI model will not understand the new patterns and it will start to make incorrect predictions. Since it was working on the old data it won’t handle the new information properly.

But checking these AI models it kinda boring and not everyone is eager to do it. So we Automate some things,

3. Use Automated Alerts: Automating some tools that can help us look for sudden drops in the accuracy of the model. It will detect the issue and send an Alert message to the main headquarters. This will allow problems to be found quickly and to be fixed quickly as well.

Why Does Model Drift Happen?

There is a need to understand why this even happens. We need to look for:

1. Data Quality Issues: Data is the main source of information for the AI models. If the given data has some errors or there are some missing values in it. Overall, If the data is wrong. This will lead to incorrect results.
2. Changes in Input Data: Updating the training data is a must. Since data is important it needs to be maintained properly for the model to make predictions more accurate.
3. Changes in Output Labels: The data which is given to the model is in the form of patterns. If the pattern which is used to make the predictions changes. The output of the model will give incorrect results.

How to Fix Model Drift

Now you know that the AI model is making some ridiculous mistakes in the predictions. So How do we Fix them?

1. Update the Model with New Data: We need the new data or the latest information. So that the AI models do not have the choice not to make the incorrect predictions. This will help the model to be up to date and to make accurate predictions.
2. Use Adaptive Learning: As we know we can automate some tools. So that we do not need to manually input the latest information. This will make it more accurate without the help of human interaction.
3. Improve Feature Selection: The AI model uses some patterns to make the decisions and predictions. If the patterns are outdated the model will give wrong results.
4. Enhance Data Processing: If the data has some kind of error like missing values. The model may give incorrect results. Hence AI model needs well-structured data to work properly.

How to Keep an AI Model Accurate and Reliable

We already know that the AI models require a daily dose of the latest data for making the correct predictions. Here are some simple ways to keep them working properly:

1. Test Before Using a New AI Model: If we are completely making the new model we need to check the data for the old and the new one. There is a possibility that the newer model is worse than the older one. So we need to check before deploying the newer model.
2. Keep Backups of Old AI Models: After we update the older model with the new one. It’s good practice to keep the backup of the old model. Because there is also a possibility that the newer model will not work as we expected. So we can easily go back to the old one with just a single click.
3. Let Humans Supervise AI Decisions: Since we made the AI models. We also need to supervise it. We did automate some tools for it but those tools need to be checked regularly. This will make the AI model be more reliable.
4. Make AI Decisions Easy to Understand: It is also important to know how AI models these predictions. If the model after deploying gave a wrong answer. The user should be able to understand it as well. This will help users be more trusted towards the model.

Conclusion: The Key to Reliable AI

Nowadays, AI is the best technology to use. You can find anything on it that has been discovered until today. But sometimes, due to some problems, it gets outdated and does not give reliable and accurate information about the query. This can make users face many problems as they mostly depend on AI.

So, to make AI work properly we have to make sure some points are:

1. Check AI performance from time to time
2. Add new information to AI.
3. Use human experts’ reviews and their feedback to improve it more.
4. Make sure to check it before completely deploying it.
5. save old version copies as a backup.
6. Make the AI easier to understand.
7. Automate updates for better accuracy

Imagine a student who is bad at studying. With time and practice, he improves his flaws and becomes a good student, but to be at the top of his class, he has to improve and learn over time. Similarly, fixing errors makes AI better, but to become accurate, AI should learn and improve with time and stay useful.

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