Designing Machine Learning Pipelines (MLOps) and Their Testing
MLOps, short for machine learning (ML) operations, is a set of practices and techniques aimed at streamlining the deployment, management, and monitoring of ML models in production environments. It borrows concepts from the DevOps (development and operations) approach, adapting them to the unique challenges posed by ML.
The main goal of MLOps is to bridge the gap between data science and operations teams, fostering collaboration and ensuring that ML projects can be effectively and reliably deployed at scale. MLOps helps to automate and optimize the entire ML life cycle, from model development to deployment and maintenance, thus improving the efficiency and effectiveness of ML systems in production.
In this chapter, we learn how ML systems are designed and operated in practice. The chapter shows how pipelines are turned into a software system, with a focus on testing ML pipelines and their deployment at Hugging Face...
