Quickstart#
MLOps, or Machine Learning Operations, is a set of practices that aims to streamline and optimize the lifecycle of machine learning models. It integrates elements from machine learning, DevOps, and data engineering to enhance the efficiency and effectiveness of deploying, monitoring, and maintaining ML models in production environments. MLOps enables developers to streamline the machine learning development process from experimentation to production. This includes automating the machine learning pipeline, from data collection and model training to deployment and monitoring. This automation helps reduce manual errors and improve efficiency. Our team added this feature to Kiroframe. It can found in the MLOps section. This section provides everything you need to successfully work with machine learning models.
Use the 
Community documentation to get a brief description of each page.
There are two essential concepts in Kiroframe MLOps: tasks and runs:
-
A run is a separate execution of your training code. A new run entry appears in Kiroframe for each run. Run is a single iteration of a task. This can include the process of training a model on a given dataset using specific parameters and algorithms. Each run records the settings, data, results, and metrics, allowing researchers and developers to track model performance and compare different approaches.
-
A task allows you to group several runs into one entity so that they can be conveniently viewed.
Follow this sequence of steps to successfully work with MLOps: