Building real-world AI tools requires working with data. The challenge? Traditional data architectures often act as stubborn filing cabinets – they simply can’t accommodate the volume of unstructured data we generate.
From customer service and generative AI-powered recommendation engines to drone deliveries and AI-driven 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 demands 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, be it the size of an object or an audio clip, needs to be of high quality. The engineering part — the process of selecting and converting these raw observations into desired features so they can be used in supervised learning — becomes critical to designing and training new ML approaches so they can tackle new tasks.
This process involves constant iteration, feature versioning, flexible architecture, solid domain knowledge, and interpretability. Let’s explore these elements further.
Head of Global Practice for Insights and Analytics at Nisum.
A suitable data architecture simplifies complex processes
A well-designed data architecture ensures that your data is available and accessible for feature engineering. Key components include:
1. Data storage solutions:Balancing data warehouses and data lakes.
2. Data pipelines:Using tools like AWS Glue or Azure Data Factory.
3. Access control:Ensure data security and proper use.
Automation can significantly ease 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), when a query needs to be executed, only the relevant partitions or columns are accessed and processed in parallel on multiple machines.
Automated data validation, feature lineage, and schema management within the architecture improve understanding and promote reuse across models and experiments, further increasing efficiency. This requires setting defined expectations for your data, such as format, value ranges, missing data thresholds, and other constraints. Tools like Apache Airflow help you integrate validation checks, while Lineage IQ supports tracking feature source, transformations, and destination. The key is to always store and manage evolving schema definitions for your data and features in a central repository.
A robust data architecture prioritizes cleansing, validation, and transformation steps to ensure data accuracy and consistency, helping to streamline feature engineering. Feature stores, a type of centralized feature repository, are a valuable tool within a supporting data architecture. The more complex the architecture and feature store, the more important it is to have clear ownership and access control, which simplifies workflows and strengthens security.
The role of feature stores
Many ML libraries offer predefined 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 designed correctly, they may not provide dynamic transformations or techniques that meet your requirements. You will likely need a centralized feature store to manage complexity and consistency.
Having a feature store simplifies sharing and avoids duplication of effort. However, setting up and maintaining it requires additional IT infrastructure and expertise. Instead of relying on the pre-built library vendor’s existing coding environment to define feature metadata and contribute new features, with a feature store, internal data scientists have the autonomy to act in real time.
There are many elements to consider when looking for a feature store that can fulfill your specific tasks and integrates well with your existing tools. Not to mention the store’s performance, scalability, and licensing terms – are you looking for something open source or 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 a product, it’s important to check reviews and version history. Does the store maintain backward compatibility? Is there official documentation, support channels, or an active user community with troubleshooting resources, tutorials, and code examples? How easy is it to learn the store’s syntax and API? These are the types of factors to consider when choosing the right store for your feature engineering tasks.
Balancing interpretability and performance
Striking a balance between interpretability and performance is often a challenge. Interpretable features are easy for humans to understand and directly relate to the problem being solved. For example, a feature called “F12,” or one like “Customer_Age_in_Years,” will be more representative and easier to interpret. However, complex models may sacrifice some interpretability to improve accuracy.
For example, a model that detects fraudulent credit card transactions might use a gradient boosting machine to identify subtle patterns across multiple features. While this is more accurate, the complexity makes it difficult 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 preprocessing tasks developers face. However, like a chef in a well-thought-out kitchen, automating data structuring into a well-designed architecture significantly improves efficiency. Equip your team with the tools and expertise to assess your current processes, identify gaps, and take practical 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. These 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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