Using pre-trained models in real-time applications can come with several limitations. Here are a few important ones to consider:

  1. Generalization: Pre-trained models are trained on a large dataset that may not perfectly represent the specific domain or problem of interest. This can lead to limitations in generalization and accuracy when applied to real-time scenarios. Fine-tuning or transfer learning techniques may be required to adapt the model to the specific problem domain.

  2. Latency: Pre-trained models are typically large and computationally expensive. In real-time applications, low latency is essential to provide prompt responses. Running complex pre-trained models in real time may require significant computational resources, which can lead to increased latency and decreased efficiency.

  3. Hardware limitations: Pre-trained models may have been trained and optimized for specific hardware configurations. Deploying these models on different hardware platforms may result in performance degradation or even incompatibility issues.

  4. Bias and fairness: Pre-trained models can inherit biases present in the training data, which may not align with real-time applications. If the pre-trained model was trained on data with certain biases, such biases can be perpetuated during real-time inference and impact fairness and inclusivity in the application.

  5. Lack of customization: Pre-trained models are designed to be generic and versatile. While they can provide a strong starting point, they may lack the ability to capture specific nuances of a particular application. Fine-tuning or retraining the model on application-specific data is often necessary to achieve optimal performance.

  6. Model drift: The landscape of real-time applications can change over time. Pre-trained models may not adapt well to new data distributions or evolving trends in the application domain. Regular updates and continuous training on new data are essential to mitigate model drift and maintain optimal performance.

  7. Privacy and security: Pre-trained models are typically trained on large-scale datasets that may contain sensitive information. Implementing pre-trained models in real-time applications should consider the potential privacy and security risks associated with the data used to train the models.

  To address these limitations, careful consideration of model selection, fine-tuning, optimization, hardware compatibility, and continuous monitoring is necessary when utilizing pre-trained models in real-time applications.