AI & Data Science

Classification is based on pattern, content and semantic analysis. It enables organisations to work more efficiently, improve their processes and gain a competitive advantage.

Classification in machine learning is the process of training a model to make predictions about the category or class of data points. The data is first collected and processed to train an appropriate classification model, such as decision trees, SVM, Naive Bayes, logistic regression or neural networks. The model is evaluated using various metrics such as accuracy, precision, recall and F1 score. Hyperparameter tuning is used to optimise the model and improve its ability to generalise to unknown data. Finally, the model can make class predictions for new, unknown data.

RISE uses advanced OCR technology, computer vision and intelligent algorithms to automatically extract data from documents. This enables organisations to improve business processes, save time and resources and make data-driven decisions.

This method of targeted data extraction is particularly useful for extracting structured data from unstructured documents and making it usable for further analysis or applications. However, it often requires careful pre-processing and adaptation of the models to the specific requirements and format of the documents. Entities or patterns are identified using Named Entity Recognition (NER) or Regular Expressions (Regex). Contextual analysis helps to verify that the extraction is correct.

By using data cleansing, normalisation and feature engineering technologies, we can improve the quality of the results and ensure that the algorithms are accurate, reliable and scalable.

Pre- and post-processing steps ensure that the machine learning model is trained on clean, well-prepared data, and that its predictions and probabilities are correctly calibrated and scored. In pre-processing, data cleansing (missing values, outliers, duplicates) is particularly important to obtain a good training set. Data normalisation (scaling, transformation) must also be performed. We use feature engineering to improve model performance and avoid overfitting.

Modern OCR systems often use deep learning models, particularly Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) or Transformers for sequence modelling, to understand the relationships between letters and words.

These models are trained on large amounts of annotated data to capture the variety of fonts, font sizes and text formats to ensure high recognition accuracy under different conditions.

Hardware, especially graphics processing units (GPUs), play a critical role in the development and implementation of machine learning (ML) and especially deep learning (DL) models.GPUs are designed for parallel processing, allowing them to perform thousands of operations simultaneously.

Modern GPUs have specialised units, such as tensor cores, that are specifically optimised for DL computations. In addition, multiple GPUs can be combined to train models in parallel or on larger datasets. By speeding up calculations, GPUs enable faster training of models, especially large neural networks.

AIR uses advanced open source tools for model development, including PyTorch, Scikit-Learn, DVC and Docker. Developed models are run as Docker containers in a dynamically scalable Kubernetes environment and integrated into business processes via HTTP APIs.

During operation, our models are automatically monitored by collecting metrics. We use OpenSearch, Prometheus and Grafana to visualise these metrics and send automated notifications of important events. AIR enables organisations to gain valuable insights from their data and develop innovative products and services.