Machine learning applications are increasingly vital for organizations seeking innovation and competitive advantages by automating processes and enhancing user experiences through AI. However, deploying machine learning models successfully is complex and fraught with challenges, requiring more than just software development skills. Key to overcoming these challenges are accessible data, collaboration between data science and engineering teams, and a strategic approach to project management. At the Convergence 2022 Conference, experts emphasized the importance of understanding business requirements, monitoring deployed models for performance, and maintaining agility in organizational processes. They also highlighted the significance of feedback systems and treating models as commodities to ensure quality. Tools like AWS Sagemaker, MLflow, and Spark were recommended for building scalable ML models, while diverse frameworks were noted for their utility and flexibility in experimentation and deployment. The conference promises further insights from industry leaders on effectively managing machine learning projects to create business value.