Key Objectives and Principles For Building Predictive Models On Big Data (Neustar)

It seems like you’re looking for a way to streamline feature engineering in your data projects, making it more efficient and scalable. Our approach at Qubole might provide some insights into how you can achieve this.

Configurable Pipeline:
- Consider implementing a configurable pipeline where the entire process, from data ingestion to feature engineering, is represented by a config. This eliminates the need for manual coding and allows for easy customization based on different data sets.
Automated Feature Generation:
- Aim to automate feature generation without writing a single line of code. By using templates and annotations, you can dynamically substitute parameters and generate code for computing features. This approach significantly reduces manual effort and ensures consistency across different data sets.
Scalability:
- Focus on scalability by optimizing the pipeline for large-scale data processing. Utilize cloud platforms and distributed computing technologies to handle billions of data points efficiently. By eliminating performance bottlenecks and optimizing resource utilization, you can achieve faster processing times even with massive data sets.
Function Expressions:
- Provide flexibility for users to define custom functions and expressions for feature engineering. This allows users to tailor the feature set according to specific requirements without relying on predefined functions.
Reuse and Collaboration:
- Encourage reuse and collaboration by maintaining a library of commonly used functions and expressions. This prevents duplication of effort and promotes knowledge sharing among team members working on different projects.

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