We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Tobias Sterbak: Introduction to MLOps with MLflow
Discover the basics of MLOps and the MLflow tool, learn how to track, manage, and reproduce machine learning models from development to deployment and maintenance.
- MLOps (Machine Learning Operations) involves managing the lifecycle of machine learning models, from development to deployment and maintenance.
- MLOps includes tracking, versioning, and reproducibility of models, as well as managing model dependencies and using a standardized format for packaging models.
- MLflow is a tool for MLOps that provides a simple, Python-based API for tracking and managing machine learning models.
- MLflow includes three main components: tracking, where it collects information about the training and validation of models; models, which is a specification of how models can be packaged and reproduced; and artifacts, which is a way to manage and store model artifacts.
- In MLflow, you can track and manage the lifecycle of your models, including the ability to version your models and track their dependencies.
- MLflow also includes a client API that allows you to access and interact with the tracking server and the model registry.
- The goal of MLOps is to make it easier to manage and maintain machine learning models, and to provide a way to reproduce and track the results of your models over time.
- MLflow is designed to be used in conjunction with other tools and platforms, such as Databricks and Azure ML, to provide a complete end-to-end solution for MLOps.
- In the talk, the speaker demonstrated how to use MLflow to track and manage the lifecycle of a machine learning model, including how to track metrics and parameters, and how to version and reproduce the model.