Building Scalable AI Solutions with Python: Best Practices and Innovations
DOI:
https://doi.org/10.15680/IJCTECE.2024.0701003Keywords:
Scalable AI, Python, Machine Learning, Big Data, Distributed Computing, Cloud Computing, AI Deployment, Data Management, Performance OptimizationAbstract
As artificial intelligence (AI) technologies continue to evolve, the demand for scalable solutions that can efficiently handle large datasets, complex computations, and real-time requirements grows significantly. Python, with its extensive libraries, frameworks, and flexible ecosystem, has become a go-to language for developing AI systems. This paper explores best practices and innovations in Python for building scalable AI solutions, focusing on optimizing computational resources, managing large-scale datasets, and ensuring efficient deployment in production environments. By incorporating best practices, leveraging Python’s scalability features, and adopting emerging innovations, developers can create AI systems that are not only effective but also scalable and maintainable. The paper outlines various tools, methodologies, and techniques that can be employed to design AI solutions that meet the performance, cost, and scalability requirements of modern AI applications.
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