<ul><li>Data quality is crucial for building accurate AI models. Relevant and clean data are essential for optimal performance.</li><li>Balancing AI model accuracy and explainability is important. Transparency promotes trust, but too much transparency may raise ethical concerns.</li><li>Consider compute resources when training AI models. Well-optimized and smaller models can perform equally well as large ones.</li><li>Deployment is a challenge in real-world applications. APIs, containerization, optimization, and scaling are necessary for deploying AI models.</li><li>A well-designed user interface is crucial for AI applications. Frameworks like Streamlit can enhance the user experience.</li></ul>

5 Key Lessons I Wish I Knew Before Building AI Projects

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