Building real-world AI tools requires getting your hands dirty with data. The challenge? Traditional data architectures often act like stubborn filing cabinets, they just don’t accommodate the volume of unstructured data we are generating.
From generative AI-powered customer service and recommendation engines to AI-powered drone deliveries and supply chain optimization, Fortune 500 retailers like Walmart deploy dozens of AI and machine learning (ML) models, each reading and producing unique combinations of datasets. This variability demands tailored data ingestion, storage, processing, and transformation components.
Regardless of the data or architecture, poor-quality features directly impact your model’s performance. A feature, or any measurable data input, whether that’s the size of an object or an audio clip, must be of high quality. The engineering part—the process of selecting and converting these raw observations into desired features so that they can be used in supervised learning—becomes critical to designing and training new ML approaches so that they can tackle new tasks.
This process involves constant iteration, feature versioning, flexible architecture, strong domain knowledge, and interpretability. Let’s explore these elements further.
Proper data architecture simplifies complex processes
A well-designed data architecture ensures your data is readily available and accessible for feature engineering. Key components include:
1. Data storage solutions: Balancing data warehouses and lakes.
2. Data pipelines: Using tools like AWS Glue, or Azure Data Factory.
3. Access control: Ensuring data security and proper usage.
Automation can significantly ease the burden of feature engineering. Techniques like data partitioning or columnar storage facilitate parallel processing of large datasets. By breaking data into smaller chunks based on specific criteria, like customer region (e.g., North America, Europe, Asia), when a query needs to be run, only the relevant partitions, or columns, are accessed and processed in parallel across multiple machines.
Automated data validation, feature lineage, and schema management within the architecture enhance understanding and promote reusability across models and experiments, further boosting efficiency. This requires setting set expectations for your data such as the format, value ranges, missing data thresholds, and other constraints. Tools like Apache Airflow help you embed validation checks while Lineage IQ supports origin, transformations, and destination tracking of features. The key is to always store and manage the evolving schema definitions for your data and features in a central repository.
A strong data architecture prioritizes cleaning, validation, and transformation steps to ensure data accuracy and consistency, which helps to streamline feature engineering. Feature stores, a type of centralized repository for features, are a valuable tool within a data architecture that supports this. The more complex the architecture, and feature store, the more important it is to have clear ownership and access control, simplifying workflows and strengthening safety.
The role of feature stores
Many ML libraries offer pre-built functions for common feature engineering tasks, such as one-hot encoding and rapid prototyping. While these can save you time and ensure that features are engineered correctly, they might fall short of providing dynamic transformations and techniques that meet your requirements. A centralized feature store is likely what you need for managing complexity and consistency.
Having a feature store streamlines sharing and avoids duplication of effort. However setting it up and maintaining it requires additional IT infrastructure and expertise. Rather than relying on the pre-built library provider’s existing coding environment to define feature metadata and contribute new features, with a feature store, in-house data scientists have the autonomy to action these in real-time.
There are lots of elements to consider when finding a feature store that can fulfill your specific tasks, and integrate well with your existing tools. Not to mention the store’s performance, scalability, and licensing terms — are you looking for open-source or something commercial?
Next, make sure your feature store is suitable for complex or domain-specific feature engineering needs, and validate what it says on the tin. For example, when choosing any product, it’s important to check the reviews and version history. Does the store maintain backward compatibility? Is there official documentation, support channels, or an active user community for troubleshooting resources, tutorials, and code examples? How easy is it to learn the store’s syntax and API? These are the sorts of factors to consider when choosing the right store for your feature engineering tasks.
Balancing interpretability and performance
Achieving a balance between interpretability and performance is often challenging. Interpretable features are easily understood by humans and relate directly to the problem being solved. For instance, a feature named “F12,” one like “Customer_Age_in_Years,” will be more representative — and interpretable. However, complex models might sacrifice some interpretability for improved accuracy.
For example, a model detecting fraudulent credit card transactions might use a gradient boosting machine to identify subtle patterns across various features. While more accurate, the complexity makes understanding each prediction’s logic harder. Feature importance analysis and Explainable AI tools can help maintain interpretability in these scenarios.
Feature engineering is one of the most complex data pre-processing tasks developers endure. However, like a chef in a well-thought-out kitchen, automating data structuring in a well-designed architecture significantly enhances efficiency. Equip your team with the necessary tools and expertise to evaluate your current processes, identify gaps, and take actionable steps to integrate automated data validation, feature lineage, and schema management.
To stay ahead in the competitive AI landscape, particularly for large enterprises, it is imperative to invest in a robust data architecture and a centralized feature store. They ensure consistency, minimize duplicates, and enable scaling. By combining interpretable feature catalogs, clear workflows, and secure access controls, feature engineering can become a less daunting and more manageable task.
Partner with us to transform your feature engineering process, ensuring your models are built on a foundation of high-quality, interpretable, and scalable features. Contact us today to learn how we can help you unlock the full potential of your data and drive AI success.
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Complex AI models need clean data. Learn how to with better feature engineering.
