After prioritizing use cases for experimentation, AltaML follows a systematic approach that includes a use case feasibility assessment, balancing ML model performance, cost, effort, and risk to address the business problem. Activities such as business context definition, data assessment, machine learning approach assessments, and use case feasibility assessments are conducted. AltaML dynamically and iteratively extracts insights from data, conducting machine learning modeling while considering ethical and bias risks. Regular communication and collaboration occur, with progress updates, feedback sessions, and collective development of the next steps. A thorough quality assurance step ensures high-quality deliverables. This phase concludes with a final report, executive overview presentation, lightweight business case, and estimates for subsequent steps.