This article discusses how organizations can use Amazon Comprehend, an AI/ML service, to build and optimize custom classification models. It provides guidelines on data preparation, model creation, and model tuning. The article also explores techniques for handling underrepresented data classes and mentions the cost of using Amazon Comprehend.
Artificial intelligence (AI) and machine learning (ML) have been widely adopted by business and government organizations. The use of natural language processing (NLP) and user-friendly AI/ML services like Amazon Textract, Amazon Transcribe, and Amazon Comprehend has made it easier to process unstructured data. Amazon Comprehend in particular allows companies to build classification models and gain valuable insights from their data.
Building and optimizing a custom classification model using Amazon Comprehend involves several steps. First, the training dataset must be carefully curated to ensure the best results. Then, the model can be created and performance metrics such as accuracy, precision, recall, and F1 score can be analyzed and adjusted. This includes examining factors like the confusion matrix and prediction probabilities. Additionally, data preparation should follow certain guidelines, such as having a minimum of 10 samples per label and using a balanced distribution of data.
To build a custom classification model in Amazon Comprehend, begin by creating a SageMaker notebook instance if one is not already available. After that, prepare the data by following the provided instructions, which utilize a dataset called the Toxic Comment Classification dataset. Once the data is prepared, run the necessary commands to download the required artifacts.
Next, the custom classification model can be built within the Amazon Comprehend console. Specify the model name, version, and parameters such as the location of the training and test datasets. Then, training can begin. Once the model is trained and created, its performance metrics can be reviewed. This includes measures like precision, recall, and accuracy, as well as creating an analysis job to gather prediction probabilities for each data point.
The optimized thresholds for each class can be determined through the use of the Model-Threshold-Analysis.ipynb notebook. From there, any underrepresented classes within the dataset can be addressed using oversampling techniques, demonstrated in the Oversampling-underrepresented.ipynb notebook.
As for cost, the pricing for Amazon Comprehend is based on the number of characters processed. Clearing up and deleting existing resources is also important to avoid additional costs.
In conclusion, this document provides insights and solutions for building and optimizing custom classification models using Amazon Comprehend. Proper data curation, model tuning, and mitigation of underrepresented data classes are key factors in achieving the best possible performance metrics. Amazon Comprehend serves as a valuable tool for organizations seeking to gain deeper insights from their unstructured data.
Action items:
1. Create an Amazon SageMaker notebook instance (Assigned to: Executive Assistant)
2. Download the required artifacts for the post (Assigned to: Executive Assistant)
3. Run the Data-Preparation notebook (Assigned to: Executive Assistant)
4. Build a custom classification model using the curated training and test datasets (Assigned to: Executive Assistant)
5. Tune the model for performance using the analysis jobs (Assigned to: Executive Assistant)
6. Use the Model-Threshold-Analysis notebook to observe thresholds (Assigned to: Executive Assistant)
7. Use the Oversampling-underrepresented notebook to optimize datasets (Assigned to: Executive Assistant)
8. Clean up resources when finished (Assigned to: Executive Assistant)
