Here are some strategies to reduce costs for data analytics and generative AI using customized applications: Data Acquisition and Storage: Optimize data collection: Focus on gathering only the data that is necessary for your specific needs. Avoid extraneous data that increases storage and processing costs. Leverage cloud-based storage: Cloud platforms like AWS, Azure, and GCP offer scalable and cost-effective storage solutions for large amounts of data. Utilize data compression: Compress your data to reduce storage requirements and data transfer costs. Implement data lifecycle management: Develop a system for archiving, deleting, and purging data that is no longer needed. Data Processing and Analysis: Choose efficient algorithms and libraries: Use algorithms and libraries that are optimized for your specific data types and tasks. Avoid using unnecessarily complex or resource-intensive algorithms. Utilize parallel processing: Leverage parallel processing technique...
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