Navigating the journey from building machine learning (ML) models to deploying them in production can often be a rocky road. It’s an essential yet complex process where data scientists and engineers must bridge their knowledge gap. Data scientists, adept at creating models, might stumble when it comes to production deployment. On the other hand, engineers may struggle with the continuous iteration of ML models, leading to inefficient, error-prone operations.

Consider this specific scenario: you have just created an ML model for text summarization, which performs brilliantly in tests. You plan to share this model with your team to build an application on top of it. However, shipping this model poses a unique challenge: ensuring your team can use your model without fiddling with code or environment setups.