To build true AI tools, you need to get your hands dirty with data. The challenge? Traditional data architectures often act like stubborn filing cabinets; they simply can’t handle the amount of unstructured data we generate.
From generative AI-driven 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 data sets. This variability requires custom data ingestion, storage, processing, and transformation components.
Regardless of the data or architecture, poor quality features directly impact the performance of your model. A feature, or any measurable data input, whether it’s the size of an object or an audio clip, needs to be high quality. The engineering part—the process of selecting and transforming these raw observations into desirable features so they can be used in supervised learning—becomes crucial for designing and training new ML approaches to tackle new tasks.
This process involves constant iteration, feature versioning, flexible architecture, strong domain knowledge, and interpretability. Let’s explore these elements further.
Global Practice Head of Insights and Analytics at Nisum.
A good data architecture simplifies complex processes
A well-designed data architecture ensures that your data is readily available and accessible for feature engineering. Key components include:
1. Data storage solutions: Balancing between data warehouses and data lakes.
2. Data Pipelines: Using tools such as AWS Glue or Azure Data Factory.
3. Access control: Ensuring data security and correct use.
Automation can significantly reduce the burden of feature engineering. Techniques such as data partitioning or columnar storage facilitate parallel processing of large data sets. By splitting data into smaller chunks based on specific criteria, such as the customer’s region (e.g., North America, Europe, Asia), only the relevant partitions or columns are accessed and processed in parallel across multiple machines when a query needs to be executed.
Automated data validation, feature lineage, and schema management within the architecture improve understanding and promote reusability across models and experiments, further increasing efficiency. This requires you to set expectations for your data, such as format, value ranges, missing data thresholds, and other constraints. Tools like Apache Airflow help you embed validation checks, while Lineage IQ supports feature origin, transformation, and destination tracking. 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 cleansing, validation, and transformation steps to ensure data accuracy and consistency, which helps 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, simplify workflows, and strengthen security.
The role of feature stores
Many ML libraries provide pre-built functions for common feature engineering tasks, such as one-hot encoding and rapid prototyping. While these can save you time and ensure features are designed correctly, they may fall short in providing dynamic transformations and techniques that meet your requirements. A centralized feature store is likely what you need to manage complexity and consistency.
A feature store streamlines sharing and eliminates duplication of effort. However, setting it up and maintaining it requires additional IT infrastructure and expertise. Instead of relying on the pre-built library provider’s existing coding environment to define feature metadata and contribute new features, a feature store gives in-house data scientists the autonomy to execute in real time.
There are many elements to consider when finding a feature store that can handle your specific tasks and integrate well with your existing tools. And that’s without even talking about the store’s performance, scalability, and licensing terms — are you looking for open-source or commercial?
Next, make sure your feature store is suited for complex or domain-specific feature engineering needs and validate what it says on the tin. For example, when choosing a 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 samples? How easy is it to learn the store’s syntax and API? These are the types of factors you should consider when choosing the right store for your feature engineering needs.
Balance between interpretability and performance
Striking a balance between interpretability and performance is often a challenge. Interpretable features are easily understood by humans and directly relate to the problem being solved. For example, a feature named “F12,” a feature like “Customer_Age_in_Years,” will be more representative — and more interpretable. However, complex models may sacrifice some interpretability for better accuracy.
For example, a model that detects fraudulent credit card transactions might use a gradient boosting engine to identify subtle patterns across different features. While more accurate, the complexity makes it harder to understand the logic behind each prediction. 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 that developers must endure. However, just like a chef in a well-designed kitchen, automating data structuring in a well-designed architecture significantly improves 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, especially for large enterprises, it is imperative to invest in a robust data architecture and a centralized feature store. They ensure consistency, minimize duplication, and enable scalability. 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 and ensure 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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