Marching towards MLOps Utopia

Kubeflow Pipelines give you the connective tissue to train models with various frameworks, iterate on them, and then eventually expose them for the purpose of serving. This means our entire Model Development Lifecycle lives within our KubeFlow pipeline components and definitions. We now have the power to be intentional and declarative with how we want our models to be developed as well as how we provide feedback loops to our data science teams. This gives us the capacity to further improve not only the data science function code but the pipeline descriptions themselves in order to respond to our ever-growing business demands. This lays the foundation for the Continuous Integration and Continuous Deployment processes which ultimately push and support models in production. This can become quite daunting if you are taking the manual approach. What works for your organization today might suffer the technical debt-ridden test of time as you begin to scale and introduce essential complexity that comes from improved velocity and feature offerings. Adopting an effective and self-sustainable MLOps culture and platform solution introduces technical stability, platform longevity, and a higher degree of team collaboration and model quality into an enterprise. The principles and concepts presented in this course are just the foundation for a much larger journey that Kubeflow will facilitate.

We hope you enjoyed this course and are as excited about the future of MLOps as we are.

If you would like to explore further solutions by attending Instructor-Led presentations: https://www.arrikto.com/kubeflow-mlops-events

If you would like to explore MLOps further please reach out to us for a free workshop: https://academy.arrikto.com/contact