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With cloud and edge computing technologies developing rapidly, it has highly impacted the deployment and scalability of machine learning models. In a world where AI-driven solutions become more integral across industries, optimization of the deployment of machine learning models in both cloud and edge environments is the only way to maximize performance, scalability, and efficiency. This paper explores frameworks, challenges, and best practices in optimizing machine learning model deployment on cloud and edge platforms. The paper talks about the advantages of cloud and edge computing for machine learning, which include scalability, reduced latency, and improved data privacy, while also addressing resource constraints, network limitations, and real-time processing demands. The paper highlights the popular deployment frameworks, such as Tensor Flow, PyTorch, and special edge devices tools like Tensor Flow Lite and PyTorch Mobile, which make the embedding of machine learning models in the cloud and edge environment feasible. Furthermore, the paper explores the intricacies and solutions of smooth deployment, online updating of models, and platform independence, which makes the AI solution scalable and applicable for a broad spectrum of applications, such as industrial automation, healthcare, smart cities, and autonomous vehicles. This analysis provides a paper that will offer insights on how organizations can build efficient, scalable AI solutions by using both cloud and edge resources to meet the growing demands of modern AI applications.
Keywords: Machine Learning, Cloud Computing, Edge Computing, Model Deployment, Scalability and AI Frameworks.
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