Machine learning(ML) is a branch of artificial intelligence that focuses on developing algorithms that can learn using data. While ML gets a lot of attention, implementing ML models (their deployment and maintenance) requires much more than programming skills.

Introducing MLOps, the short term for machine learning operations. MLOps represents a set of practices that simplify workflow processes and automate machine learning and deep learning deployments. For example, in the case of a smart city, a good use case is a model that automatically sends alerts when there are accidents. It constantly retrains based on new traffic data and behaves differently during bank holidays or seasons.

MLOp accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale. In other words, MLOps enables you to ship models faster, ensuring portability and reproducibility. Navigating the landscape of MLOps solutions can be daunting. There is no one size fits.

If you want to understand current MLOps trends, watch this video to find out how to choose the right solution and learn about Charmed Kubeflow. During the video, you will learn from Canonical’s AI experts, Maciej Mazur, Principal AI/ML Engineer, and Andreea Munteanu, MLOps Product Manager, based on real customer questions and requests collected by Adrian Matei, Sales Representative.

Key moments:

01:22 Introduction

03:02 What is the business value?

07:12 MLOps architecture overview

14:37 Demo: Charmed Kubeflow

Charmed Kubeflow: https://charmed-kubeflow.io/

A guide to MLOps: https://ubuntu.com/engage/mlops-guide

Learn more:
https://ubuntu.com/blog/what-is-mlops

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