Machine learning (ML) is a transformative technology with significant business impact across industries, yet its full potential is often challenging to realize due to the complexities involved in developing and deploying ML products. Organizations, particularly AI startups, must grasp the entire ML lifecycle to build effective, scalable, and secure ML solutions. This lifecycle encompasses defining business problems, translating them into ML problems, preparing datasets, training models, deploying them, and continuously monitoring and maintaining performance. Each stage requires careful planning, from iterating on problem definitions and aligning them with business goals to choosing appropriate modeling techniques, ensuring data quality, and strategically deploying models while considering ethical and legal standards. Continuous monitoring and retraining are essential to maintain model performance amidst data and model drift, thus ensuring the ML products remain robust and effective in dynamic environments. By systematically navigating these stages with feedback and iteration, organizations can increase the success rate of their ML projects, ultimately enhancing business outcomes.