_predictive models

Predictive modelling is about using algorithms to generate an accurate prediction, for example, the prediction of credit card fraud transactions, the prediction of the presence of a mineral in core samples based on the spectral absorption, and a recommendation system that predicts which products a customer is likely to be interested in. Building a predictive model involves using machine learning to learn from patterns, relationships and trends from the data and predicting based on new data.

_natural language processing

Natural language processing (NLP) has come a long way in recent years, with open-sourced pre-trained models available for commercial use. NLP techniques involve learning from patterns in text data and building models that can predict sentiment, perform text completion, extract named entities such as names and dates. NLP models and analysis can provide useful, automated tools and insights that may otherwise require a human to manually read through a large volume of text documents.

_model deployment

The last step of a machine learning project is the deployment of the model on a suitable platform. The goal here is to make the model easy to use, and its results available at a suitable frequency. A machine learning model can be deployed so that it runs on-demand and as required, or automatically at a specified frequency. Our data scientists are experienced with cloud technologies including AWS, Microsoft Azure and Google Cloud. Whether you have existing servers you would like us to integrate the model with, or deploy from scratch on the cloud, we will
work with you to find the best solution.

_time series forecasting

Data that is a series of measurements or observations over time is known as time series data. This data often contains weekly, monthly or yearly patterns, or some form of structure in its history, which can be used to train an algorithm to predict the future. The more consistent the structure, the more accurate the prediction.