Meetup #36

Moving Deep Learning from Research to Production with Determined and Kubeflow

Translating the research that goes in to creating a great deep learning model into a production application is a mess without the right tools. ML models have a lot of moving pieces, and on top of that models are constantly evolving as new data arrives or the model is tweaked. In this talk, we'll show how you can find order in that chaos by using the Determined Model Registry along with Kubeflow Pipelines.

Take-aways

- What components are needed to do inference in ML - How to structure models for ML inference - How a model registry helps organize your models for easy consumption - How you can set up reusable and easy-to-upgrade inference pipelines

In this episode

David Hershey

David Hershey

Solutions Engineer, Determined AI

David Hershey is a solutions engineer for Determined AI. David has a passion for machine learning infrastructure, in particular systems that enable data scientists to spend more time innovating and changing the world with ML. Previously, David worked at Ford Motor Company as an ML Engineer where he led the development of Ford's ML platform. He received his MS in Computer Science from Stanford University, where he focused on Artificial Intelligence and Machine Learning.

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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.