Maximizing Efficiency and Scalability in Open-Source MLOps: A Step-by-Step Approach
This talk presents a novel approach to MLOps that combines the benefits of open-source technologies with the power and cost-effectiveness of cloud computing platforms. By using tools such as Terraform, MLflow, and Feast, we demonstrate how to build a scalable and maintainable ML system on the cloud that is accessible to ML Engineers and Data Scientists. Our approach leverages cloud managed services for the entire ML lifecycle, reducing the complexity and overhead of maintenance and eliminating the vendor lock-in and additional costs associated with managed MLOps SaaS services. This innovative approach to MLOps allows organizations to take full advantage of the potential of machine learning while minimizing cost and complexity.
Dr. Paul Elvers is Head of AI/Data Science at Datadrivers, an IT Consulting Company in Hamburg. He graduated in Systematic Musicology & worked as a Research Fellow at the Max-Planck-Institute for empirical Aesthetics before transitioning into Data Science. He has worked for companies like Tchibo and Smartclip, before joining Datadrivers in 2021.