Developing and implementing high-quality Machine Learning (ML) solutions is undoubtedly a challenge, but thanks to our deep expertise in machine learning and advanced technology tools, we are able to deliver innovative and effective solutions that contribute to the achievement of business objectives. ML-based systems present a number of challenges that differ from those we are familiar with from traditional software engineering. The distinguishing factors of ML-based systems are the high dependence on the quantity and quality of training data, the iterative nature of the work based on experiments, and the process of learning dependence rules as opposed to defining them manually.
When carrying out an ML project, we focus first and foremost on understanding the problem and identifying the project’s business objectives. We believe that a correctly defined objective and mutual understanding is the key to a well-run project. The workflow is most often based on our proven methodology: CRISP-DM (Cross-Industry Standard Process for Data Mining), which places great emphasis on the iterative nature of the work carried out and above all on the above-mentioned understanding of the data and the problem.
