Developing a Modern ML Solution for a Packaging Manufacturer

Solution

To implement the project, we assembled a team of a Project Manager and a Machine Learning Engineer with deep Python development expertise. 

Our first move was to conduct a Proof of Concept phase, to determine whether our approach was viable. The PoC showed that using Gradient Boosting Classifiers and transformers like Text Embeddings Models was the right choice, and the development started.

We developed a Python pipeline that efficiently classifies item lines in invoices. Using AWS Textract, the tool first extracts the text and tabular data from PDF documents. Leveraging Natural Language Processing (NLP) techniques, text classification, and text embeddings, the pipeline then accurately categorizes item lines. This approach secures data accuracy and completeness.

To train and improve the model, we used labeled item lines of invoices as a data source. 

To ensure the accuracy of text classification functionality, we:

• Collected data from reliable sources and cleaned it to remove noise and inconsistencies.
• Ensured accurate labeling for each text sample to build a reliable training dataset.
• Balanced the dataset to prevent class imbalance issues that can affect model performance. 
• Split the dataset into training, validation, and test sets to evaluate the model’s performance effectively. 
• Used metrics such as precision, recall, and F1-score to measure the quality and reliability of the classification results.

Finally, we successfully integrated the model with a client’s custom CRM system via API.