Introduction to MLFlow

Companies across sectors are betting on AI to work more efficiently and drive return on investment, but before this happens they need to move their projects beyond experimentation.

Canonical supports organisations by offering machine learning operations (MLOps) solutions which can be used to develop and deploy machine learning models. MLFlow is the latest addition to our MLOps portfolio. Ideal to track experiments and their artefacts, including code, data, config and results, MLFlow is gaining ground as an industry-leading machine learning tool.

What is MLFlow?

MLFlow is an open source platform used for managing machine learning workflows. It is a crucial component of the open source MLOps ecosystem, having passed 10 million monthly downloads at the end of 2022. It has four main components that ensure experiment tracking, model registry, model deployment and code packaging.

Join our webinar to learn more about MLFlow

During this webinar, Andreea Munteanu will discuss MLFlow and Charmed MLFlow, Canonical’s distribution of the open source platform. At the end of the webinar, you will have an understanding of:

• Where MLFlow stands in the machine learning landscape
• Benefits of MLFlow
• The main differences between the upstream project and Charmed MLFlow
• Best use cases for Charmed MLFlow
• Situations when it is suitable to integrate it with other MLOps platforms
• How to build your end-to-end MLOps solution