Machine learning (ML) is an iterative, end-to-end process that begins with defining the business problem and assessing feasibility, followed by data acquisition, preparation, and exploration. Once the data is cleaned and annotated, models are trained, validated, and optimized using appropriate algorithms and evaluation metrics. The best-performing model is then deployed into a production environment where it can generate predictions or insights. Post-deployment, the model is continuously monitored for performance, accuracy, and drift, triggering retraining or updates as needed. Throughout the lifecycle, collaboration among data engineers, data scientists, ML engineers, and business stakeholders ensures alignment with organizational goals and real-world effectiveness.