Prioritizing AI Model Development at proprty.ai

First step: understanding the data

At property.ai, our first and most important task is to understand the data we work with. This means we delve into each and every data point to understand its nature, origin and potential value. Without a thorough understanding of our data, we cannot build effective and reliable AI models.

Step Two: The Search for Contexts

Once we have a solid understanding of our data, we begin to look for correlations between different data points. This is where we apply advanced analytical techniques to identify patterns and relationships that can be crucial for the development of our models.

Step Three: Establishing an End-to-End Pipeline

The next step is to create a complete pipeline. This process involves ELT (Extract, Load, Transform), modsharing, deployment, model management, model serving and model monitoring. A robust pipeline is essential to ensure that our models can be trained effectively and deployed seamlessly in real-time environments. Data Bricks helps us do all this quickly, easily and cost-effectively. In particular, the Data Bricks Catalog is an indispensable tool for keeping track of our data.

Fourth step: Training the first model

With a solid pipeline in place, we begin the training of our first AI model. In this phase, we focus on the most obvious features of our data. The aim is to create a baseline model that we can build on.

Step Five: Adding More Features

Once our baseline model is established, we begin experimenting with adding more features to improve the precision of our predictions. This is an iterative process in which each addition is carefully evaluated for its impact on model performance.

Balancing features

A key element in this process is to balance the selected features. We assess not only which features have a strong correlation with our goals, but also how exotic they are in terms of availability, technical complexity and quality. It's a delicate balancing act in which we strive to maximize model precision while dealing with the practical realities of implementing these features.

Conclusion

At property.ai, our approach to developing AI models is both methodical and innovative. We understand the importance of knowing our data in depth, identifying key relationships, building a robust pipeline, and carefully selecting and balancing the features we include. It is this combination of thoroughness and creativity that enables us to develop AI models that are not only powerful, but also relevant and applicable in the real world.