Our team is composed of senior consultants and developers with a great track record in delivering software products and services, as well as helping customers in optimising their end-to-end delivery process. Whether it’s solving a domain specific problem, developing an AI capability or implementing a stand-alone product, we do it in an incremental and iterative way, blending agile processes with strong engineering practices.
We’re focused on helping you acquire the knowledge you need to make the right decisions and improve your business.
Our methodology to deliver predictive analytics solutions and intelligent
applications efficiently is based on the Microsoft’s Team Data Science Process lifecycle (TDSP). It has been designed for data science projects that ship as part of intelligent applications, deploying machine learning and artificial intelligence models for predictive analytics. TDSP contains a distillation of the best practices and structures from Microsoft and others in the industry, designed to help companies fully realise the benefits of their analytics programs.
Thus, the major stages of our data science process are as follows:
1. Business understanding
The goals of this stage are to identify the business problems you want solved, and the data sources that help us solve those business problems. We will work with you through understanding how machine learning can add value solve your specific business challenge.
2. Data acquisition and understanding
Once we have identified the specific business problem to solve, we will explore the available data, determine whether its quality is adequate for your needs, and, optionally, set up a data pipeline to refresh the data regularly.
Having the data ready to model, we will focus on determining the optimal data features for the machine learning model and create a machine learning model that predicts the target as accurately as possible. You will be able to assess the performance of this model in a test environment, giving you the opportunity to determine whether or not the model should be deployed in production.
Now, your machine learning solution is verified and ready to move to the production phase. Our teams will develop a production-quality version of the solution and optionally integrate it with your existing system or platforms - of course, building a separate application around the machine learning model is also an option.
Together with you, we confirm that the pipeline, the model, and their
deployment in a production environment satisfy your business objectives.
This is not the end, however. We can optionally provide ongoing support for the models we deployed, continuously verifying the model’s accuracy, gathering new data and enlarging the dataset fed to the model.