In the contemporary landscape of data-driven decision-making, enterprises are increasingly turning to predictive analytics to gain valuable insights into future trends and behaviors. Predictive analytics involves extracting patterns from historical data to forecast future outcomes, enabling organizations to make proactive decisions and optimize their operations. Traditionally, predictive analytics has been performed using standalone machine learning platforms, necessitating data extraction, preprocessing, modeling, and deployment pipelines. However, this approach often introduces complexity, latency, and potential security risks due to data movement across disparate systems.
