Three Factors to Keep in Mind When Optimizing Your AI Model

Artificial intelligence is becoming a necessity. Businesses in all industries rely on it to a certain extent – from healthcare and transportation to hospitality and construction. Becoming such a key tool does mean there are certain issues, though.

For one, an AI model is only as good as its training data. The way a model is training – and the data that is used to do so – will greatly impact its success in your business. After all, as the classic principle says, “garbage in, garbage out.” But you do need to try and combat this.

It won’t be easy, of course. Optimizing an AI model requires careful planning and research. Without this, you could end up with a tool that brings chaos, not efficiency. If you want to learn how to improve your AI model, then use the three tips below.

Data Augmentation and Noise Reduction

These two aspects are crucial. While data augmentation is the practical of artificially expanding an AI training dataset with modified or synthetic data, noise reduction refers to the set of techniques used to remove errors and unwanted variations. They directly control how well an AI model learns genuine patterns, rather than memorizing irrelevant details.

Noise reduction is best completed first. This means cleaning your baseline dataset before applying synthetic variations. You should do this by removing duplicate entries, measurement errors, and such, allowing the model to learn clear foundational patterns. As a result, the “garbage in, garbage out” principle is avoided.

Then you can use transformations to simulate realistic production conditions. Color jitter in Computer Vision or synonym replacement in Natural Language Processing are common options.

However, that’s not to say all noise is negative. At times, injecting controlled noise – such as Gaussian noise – during data augmentation is worthwhile. It acts as a regularizer, which forces the network to remain resilient against minor input corruptions.

Model Pruning

There will be redundant or low-impact parameters in the neural network. They need to be removed, and model pruning is the technique to do so. Such a technique is incredibly valuable.

Not only does this shrink the file size of the model by eliminating unused parameters, it also speeds up response times. This occurs as the fewer parameters means there will be fewer mathematical calculations per prediction. If you run a real-time application, this is essential.

But there are numerous options available. You need to know the difference between unstructured and structured pruning. Unstructured removes individual weights based on tiny values. This creates sparse matrices, which can shrink the file size but might not speed up interference. On the other hand, structured pruning removes entire channels, neurons, and so on, to shrink matric dimensions. It’ll be far faster on standard hardware.

Continuous Performance Monitoring

AI models naturally degrade over time. That’s a fact. Because of this, continuous performance monitoring is necessary. It will also keep the production environment dynamic.

As real-world input data can change over time, this means it will differ significantly from the static datasets used during initial training. You need to monitor to catch these statistical shifts early on. Doing so will ensure the model is updated or retrained before accuracy plummets significantly.

While establishing a baseline and tracking data is critical, you also need to track resource metrics. Things like CPU-GPU utilization, inference latency, and throughput. Don’t forget the predictive accuracy using observability stacks, though – they must also be monitored properly.

To conclude, optimizing an AI model is no easy task. It requires a lot of research and technical know-how to do so correctly. Fortunately, this post outlines three key areas to focus your efforts. This will ensure your results are accurate going forward.