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In this extensive chapter, we journeyed through the broad landscape of AI tools, frameworks, and platforms, assessing their functionalities, strengths, and weaknesses in diverse use cases. From exploring prominent deep learning frameworks like TensorFlow, PyTorch, and Keras to delving into traditional AI tools, cloud-based platforms, edge computing, AutoML tools, AI development environments, and data management tools, we provided comprehensive insights into each one’s unique applications. The chapter also touched upon the critical debate of open source vs proprietary tools, emphasizing the significance of informed decision-making. The chapter underscores the importance of choosing the right tools for efficiency, scalability, cost-effectiveness, quality results, and seamless integration, laying out the foundations for a successful AI journey