Machine learning(ML) is a branch of artificial intelligence that focuses on developing algorithms that can learn by using data. While ML gets a lot of attention, the actual implementation of ML models (their deployment and maintenance) requires much more than programming skills.
MLOps is the short term for machine learning operations. MLOps represents a set of practices that aim to 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 data regarding the traffic, and it behaves differently during bank holidays or during different 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 are looking to understand current MLOps trends, join our webinar to find out how to choose the right solution and learn about Charmed Kubeflow.
During the webinar, 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.
We will cover:
- The main challenges when getting started with machine learning
- Key factors to consider when choosing an MLOps platform
- Deep dive into an MLOps solution: Charmed Kubeflow
- Hands-on guide on how to deploy Charmed Kubeflow
- A walkthrough of the tool, using a real use case
- Insights into the future of MLOps
Have questions? Contact us here!