Part 3/7:

Despite the wealth of data, ML scientists at Wayfair faced significant delays—each new model required the creation of custom data pipelines, often taking 4 to 6 weeks. This lag stemmed from the absence of a centralized system to manage seed data and features. The traditional setup relied heavily on reactive, siloed pipelines, leading to redundancy, inconsistency, and slow iteration cycles. The core issue was empowering ML teams to quickly access curated, high-quality data for feature creation without constant dependence on data engineering.

Understanding the ML Lifecycle and Data Bottlenecks

The ML lifecycle comprises four stages:

• Model Engineering

• Model Training

• Model Deployment

• Model Monitoring