Continuous machine learning technology allows if desired, updating the machine learning system at every instance. In a continuous frame of operation, we accept that change is inevitable. Models updated automatically at every instance are always ready, make fewer mistakes and allow more business benefits. These models can be used directly in deployment, or for continuous monitoring. When the deployed model needs to be updated, the continuous monitoring model can replace it without training and through much faster validation procedures.

On stock market data, we compare batch and continuous machine learning and demonstrate the efficiency (the computational resource benefits-CPU time) and accuracy of continuous learning compared to batch machine learning decision trees and XGBoost. For 3 months of stock data, the continuous machine learning system updated every 1 minute, has similar accuracy to XGBoost updated every 10 minutes and is 127 times faster. The continuous machine learning system is updated every 1 minute, has better accuracy than decision trees updated every 10 minutes and is 23 times faster. Considering the fact that businesses need more machine learning solutions for different tasks and the world keeps changing in unpredicted ways and always, the cloud cost savings using continuous learning will be quite significant. Continuous learning will also allow democratization of AI by letting small players benefit from AI using less infrastructure costs.