This thesis aims to provide a structured and practical guide to facilitate the integration of Machine Learning (ML) techniques into log analysis processes, with the ultimate goal of improving software reliability. In order to achieve this, the work first presents an overview of Machine Learning, introducing its fundamental concepts and the principal approaches that are relevant to log-based analysis. Although the proposed guide is designed to be tool-agnostic, the thesis includes a concise discussion motivating the adoption of outsourced solutions for ML-driven log analysis. This discussion highlights their advantages in terms of scalability, reduced operational overhead, and ease of integration within existing information systems.