An Engineering Framework for Improving Model Accuracy

Training an accurate model is one thing – but accuracy in production is a whole different challenge. 91% of ML models degrade over time. And for mission-critical use cases like fraud detection, each incremental change in model accuracy can translate to millions in business impact.

So how do you keep accuracy from eroding in production? For many ML teams, it’s cost-prohibitive to meet accuracy standards. But here’s the good news: You can improve accuracy more efficiently by taking a look at your feature engineering workflow.

Features are an important factor impacting model accuracy. So the hidden culprits behind accuracy issues are often related to feature implementation. With this framework, you’ll learn about the technical challenges that silently kill model accuracy, from data leakage to stale features. 

You’ll also get practical strategies to architect more accurate ML systems through better feature engineering practices, such as:

• Implementing point-in-time correctness to prevent data leakage across complex feature dependencies
• Architecting unified feature pipelines that ensure consistent offline/online implementation
• Monitoring approaches to detect and address feature drift before it impacts production

Whether you're building fraud detection systems, recommendation engines, or other production ML applications, this framework provides the infrastructure and implementation patterns needed to deliver accurate predictions at scale. You can improve model accuracy, while making the best use of your engineering resources.