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**Question 3:** What are the challenges associated with deploying machine learning models in resource-constrained environments, such as edge devices? How can these challenges be addressed? Deploying machine learning models in resource-constrained environments poses several challenges, including limited computing power, memory constraints, and power consumption. To address these challenges, techniques such as model compression, quantization, and pruning can be used to reduce the size and complexity of the models. Additionally, hardware acceleration and optimized software libraries can improve the performance of the models on edge devices.
