Design patterns are reusable, time-tested solutions to common problems in software engineering (View Highlight)
Here, I’d like to share a couple of patterns I’ve seen in machine learning systems. Some of them, such as process data once and evaluate before deploy, may seem basic to seasoned practitioners (View Highlight)
A key pattern when designing data pipelines is to consolidate and process raw data just once, preferably early on. (View Highlight)
Pros: Reduces redundancy and streamlines data processing jobs, making the data pipeline more efficient and maintainable (View Highlight)
Processing and aggregating data so it flexibly supports various downstream use cases can be challenging. (View Highlight)
