Overview
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Learn how to deploy Machine Learning models into production using BentoML in this comprehensive tutorial video. Explore the installation process for BentoML, save ML models to BentoML's local store, create a BentoML service, build and containerize a bento with Docker, and send requests to receive inferences. Follow along as the instructor demonstrates training a simple ConvNet model on MNIST, saving a Keras model, and running a BentoML service via Docker. Gain insights into deployment options such as Kubernetes and Cloud platforms, and access accompanying code on GitHub for hands-on practice.
Syllabus
Intro
BentoML deployment steps
Installing BentoML and other requirements
Training a simple ConvNet model on MNIST
Saving Keras model to BentoML local store
Creating BentoML service
Sending requests to BentoML service
Creating a bento
Serving a model through a bento
Dockerise a bento
Run BentoML service via Docker
Deployment options: Kubernetes + Cloud
Outro
Taught by
Valerio Velardo - The Sound of AI