Enhanced data security: Embedding ML within the database also helps to minimize security risks associated with data movement from different data sources to target and sensitive information remains within the confines of the database.
Improved efficiency: Lastly, in-database machine learning helps to minimize data transfer and processing times, leading to faster model development and deployment.
Historically, machine learning and data laos whatsapp number data analysis have functioned in separate spheres, often necessitating cumbersome data transfers between systems. This siloed approach introduces drawbacks like inefficiency, security vulnerabilities, and a steeper learning curve for non-specialists (Singh et al., 2023).
In-database machine learning emerges as a game-changer, integrating machine learning capabilities directly within database management systems (DBMSs). This streamlined approach allows users to perform tasks like model training, evaluation, and deployment entirely within the familiar database environment. By leveraging existing SQL commands and database functionalities, in-database machine learning fosters closer collaboration between data scientists, analysts, and database administrators. Additionally, it empowers a broader range of users to contribute to building and deploying models, as expertise in specialized machine learning languages is no longer a mandatory requirement.