Best practices

Best practices for continuous model training, such as training frequency, selecting sample documents, and monitoring model performance metrics.

Here are some best practices for continuous model training:

Perform regular training:

• Perform regular training cycles to ensure your model is up to date and adapts to changes in data and requirements.

• The frequency of training can vary depending on the type of data and training progress, but it is important to train regularly to maintain model performance.

Use updated sample documents:

• Use recent sample documents that are representative of the data your model will face.

• This may include adding new documents, removing outdated documents, or editing metadata to ensure the training data is current and relevant.

Select diverse samples:

• Make sure your training data covers a wide variety of scenarios and use cases to ensure the model is robust and versatile.

• Consider different variations in layouts, languages, formats, and content to ensure the model works well in different situations.

Monitor model performance:

• Regularly monitor the performance of the model using relevant metrics such as accuracy, precision, and recall.

• Analyze the results of classification tests and validation checks to identify weak points and spot opportunities for improvement.

Incorporate continuous feedback:

• Incorporate feedback from users and experts to continuously improve the model.

• Collect feedback on misclassifications or inadequate results and use this information to adjust and optimize the model.

Automate the training process:

• Automate the training process to increase efficiency and minimize human error.

• Use tools and scripts to automatically perform model training, evaluation, and updating when new data is available or changes are required.

By implementing these best practices for continuous model training, you can ensure that your model is constantly improving and achieving optimal performance to meet the needs of your use case.