Covers: theory of Model Serving
Estimated time needed to finish: 30 minutes
Questions this item addresses:
  • How can i serve a model in practice?
How to use this item?

There are several files in this folder that walk you through a hands-on exercise to containerize and serve models on AWS EC2

  • Containerizeing Flask appication and Model
  • Serve Container on AWS-EC2 instance
  • MLOps Docker development environment
Author(s) / creator(s) / reference(s)
Brendan McGivern
Programming Languages: Python
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Delivering your Models to End Users

Contributors
Total time needed: ~2 hours
Objectives
This recipe aims to walk you through steps necessary to prepare and deliver your ML models as a service
Potential Use Cases
Serving ML models to users
Who is This For ?
INTERMEDIATEData Scientists, Machine Learning Engineers
Click on each of the following annotated items to see details.
Resource Asset4/7
VIDEO 1. Inference model serving overview
  • How to serve trained models?
14 minutes
VIDEO 2. Flask Iris Model Serving
  • How to serve a trained Iris model?
14 minutes
REPO 3. Hands-on Model Serving
  • How can i serve a model in practice?
30 minutes
VIDEO 4. Serve a container on AWS ec2
  • How to serve a container on EC2?
  • How to serve a container on a VM?
12 minutes
RECIPE 5. Model Deployment
10 minutes
VIDEO 6. Flask Tutorial: Full-Featured Web App
  • How to create a web application using ML models?
17 minutes
VIDEO 7. REST API concepts and examples
  • What are REST API's?
8 minutes

Concepts Covered

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