What is MLOps?
Machine learning operations (MLOps)
As an emerging field, MLOps is rapidly gaining ground among data scientists, machine learning engineers, and artificial intelligence enthusiasts. MLOps stands for Machine Learning Operations. MLOps is a core feature of machine learning engineering focused on simplifying the process of bringing machine learning models into production and then maintaining and monitoring them. MLOps is a collaborative function that typically consists of data scientists, devops engineers, and IT staff.
What are MLOps used for?
MLOps is a useful method for creating and improving the quality of machine learning and artificial intelligence solutions. By adopting an MLOps approach, data scientists and machine learning engineers can collaborate and accelerate model development and production by implementing continuous integration and deployment (CI/CD) practices and proper monitoring, validation, and governance of ML models.
Why do we need MLOps?
Productizing machine learning is difficult. The machine learning lifecycle consists of many complex components such as data ingestion, data preparation, model training, model tuning, model deployment, model monitoring, interpretability, and more. It also requires collaboration and handoffs across teams, from data engineering to data science to machine learning engineering. Of course, strict operational rigor is required to keep all these processes in sync and working together. MLOps covers experimentation, iteration, and continuous improvement of the machine learning lifecycle.