Learn the key steps to build AI software, from data preparation to model training, testing, and deployment. Building AI software involves a structured, multi-phase process that requires careful planning and execution. This blog outlines the essential steps, from defining the problem and collecting relevant data to model training, testing, and deployment. Learn how to choose the right algorithms, preprocess data effectively, and evaluate model performance using appropriate metrics. We also explore deployment strategies and post-launch monitoring to ensure continuous improvement. Understand common challenges, such as data bias and scalability, and how to address them. Whether you're developing a recommendation engine or a smart assistant, this guide provides a clear roadmap for building reliable AI software.