MLOps is a Machine Learning (ML) engineering culture and practice that aims to unify ML system development (Dev) and ML system operation (Ops). It serves as a counterpart to the DevOps practice in classical software development which involves Continuous Integration (CI) and Continuous Deployment (CD). Practicing MLOps advocates automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.

In this workshop, we discuss how the continuous delivery principles from software engineering translate to data science and machine learning by introducing all steps of an MLOps workflow.
In the subsequent practical part, we use real data from wind turbine parks and develop an end-to-end solution from data versioning to model training to model deployment. We will focus on state-of-the-art open-source tools such as Data Version Control (DVC), MLflow and Apache Airflow.

This workshop will provide data professionals with the essential tools and knowledge to create, integrate, continuously train and deploy ML systems in production environments using open-source tools that can be used in any cloud setting.

This workshop targets professionals working with data. For the theoretical part no prior knowledge of MLOps is required. Following the practical part requires basic knowledge of the Python programming language and of the Machine Learning domain. 

The frameworks used in this workshop will be introduced to the audience during the workshop and no prior experience is necessary to benefit from the session. 

All participants need to bring their own laptops. Participants will need to have admin rights to their laptops so they can install VS Code (or any other tool to connect via SSH to a Virtual Machine).