MLOps community meetup #37! This week we talk to David Hershey Solutions Engineer at Determined AI, about Moving Deep Learning from Research to Production with Determined and Kubeflow. In this clip from the longer conversation, David tells us why feature stores can help with the process of getting models into production.
// Key takeaways:
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
// Abstract:
Translating the research that goes into 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.
// Bio:
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.
// Relevant Links
www.determined.ai
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