Coffee Sessions #107

Why and When to Use Kubeflow for MLOps

Kubeflow is an excellent platform if your team is already leveraging Kubernetes and allows for a truly collaborative experience. Let’s take a deep dive into the pros and cons of using Kubeflow in your MLOps.

Take-aways

- Kubeflow is an excellent platform if your team is already leveraging Kubernetes and allows for a truly collaborative experience - Kubeflow isn't right for every team and scenario - Kubeflow is pretty comprehensive but is still under heavy development with more to come in the future. It can still benefit from third-party OSS: e.g. MLflow and Kyverno

In this episode

Ryan Russon

Ryan Russon

Manager, MLOps and Data Science, Maven Wave Partners

From serving as an officer in the US Navy to Consulting for some of America's largest corporations, Ryan has found his passion in the enablement of Data Science workloads for companies and teams. Having spent years as a data scientist, Ryan understands the types of challenges that DS teams face in scaling, tracking, and efficiently running their workloads.

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Mihail Eric

Mihail Eric

Host

Mihail optimizes for impact. he strives to work with talented people to do amazing things. He's an engineer, researcher, and educator that has helped start teams at innovative organizations such as Amazon Alexa and RideOS. Mihail runs Pametan, a consultancy helping companies across verticals deliver machine learning and data-driven solutions to their hardest problems with a special focus on NLP, recommendation systems, tabular data, and computer vision domains. They’ve helped teams deliver 33% lift on key business metrics in <6 weeks. Mihail also built Confetti AI, the premier educational platform for training the next generation of machine learning practitioners.