Model Evaluation
Once the model is trained, we can evaluate its performance using relevant metrics such as accuracy and ROC curve:
Real-Time Prediction
Finally, we can deploy the trained model as a user-defined function (UDF) for real-time prediction:
In the past, getting insights from data laos whatsapp number data involved a lot of back-and-forth. Information needed to be moved around, analyzed by specialists, and then the results delivered back. But in-database machine learning is changing the game.
Imagine having a powerful toolbox built right into your data storage system. That’s the idea behind in-database machine learning. It lets you create “smart models” directly within your existing database. These models can analyze your data and predict future trends or uncover hidden patterns. It’s like having a crystal ball for your business, all without ever needing to move your data around.
This new approach offers several exciting benefits. First, it allows for much faster decision-making. Traditional methods often involve waiting for data transfers and external analysis, which can take time. In-database machine learning works directly with your data where it’s stored, giving you real-time insights. No more waiting around for results.