AI training with limited data: How you can optimise your AI models even with limited data

Training AI models usually requires large amounts of data. But this can often present a problem for companies in that they may not have enough suitable data available to them. Sometimes it’s a case of having too little data or that the available data is of poor quality.

So how can effective AI training be achieved with less data? This article shows you how you can develop powerful models even with limited data.

Challenges and solutions in AI training with limited data

Many AI projects fail due to insufficient data. The resulting models therefore cannot function reliably in real-world applications. Here are some strategies for overcoming this problem:

Use transfer learning

Don’t start from scratch. Use models that have already been pre-trained with large amounts of data and adapt them to your specific requirements. This can considerably reduce the need for new data.

Data augmentation

Enlarge your training data artificially by modifying existing data, such as mirroring, rotating or changing the colour saturation of images. This helps to make the model more robust without having to collect new data.

Choose the right models and techniques

If you have limited data, use simpler machine learning models such as regression models. If these do not suffice, several such models can be trained in parallel and combined using ensemble methods to create a more powerful overall system.

Apply stronger regularisation

Avoid adapting your model too specifically to the training data. This will lead to poor performance in the case of new, unknown data. Regularisation is a technique used to prevent over-adaptation to the training data and to ensure better generalisation with respect to new data.

Exam preparation provides a useful analogy here: it is more effective to gain a broad understanding of the subject matter than to memorise specific details from limited resources.

Quality over quantity

If only a limited amount of training data is available, it is worthwhile to check this data carefully and to ensure it is of high quality. This allows the model to be trained effectively even with a smaller amount of data.

Conclusion

Training effective AI models with limited data poses a challenge that requires creative solutions and techniques. By using strategies such as transfer learning, data augmentation, using simpler models and focusing on data quality, developers can create powerful and flexible AI systems even with fewer resources.

This enables companies to use AI efficiently and cost-effectively, even when large amounts of data are not available or not accessible.