Introduction
Machine Learning Operations is referred to as MLOps. The goal of MLOps is to make it easier to put machine learning models into production, manage them, and monitor them. Data scientists, ML engineers, and DevOps engineers frequently work together in MLOps, which is a collaborative role. Machine learning and DevOps, two distinct domains of software engineering, are combined to form the term MLOps.
MLOps may include anything from the data flow to the deployment of machine learning models. In some situations, MLOps implementation is just used for machine learning model deployment, but you can also find businesses that have implemented MLOps across a variety of ML Lifecycle development domains, such as exploratory data analysis (EDA), data preprocessing, model training, etc.
Python has a growing ecosystem of open-source MLOps libraries. This article lists down some of the really useful MLOps libraries in Python in no particular order.
- MLFlow
- Kubeflow
- FastAPI
- Airflow
- ZenML
- Seldon
MLFlow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It has the…
