04-deployment

4. Model Deployment

4.1 Three ways of deploying a model

4.2 Web-services: Deploying models with Flask and Docker

See code here

4.3 Web-services: Getting the models from the model registry (MLflow)

See code here

4.4 (Optional) Streaming: Deploying models with Kinesis and Lambda

Note: Since some of the steps in this video requires the use of AWS services which incur some cost on the user, it is optional to code along to this video. However, as material in Module 6 is based on the content of this video, we still highly recommended that you watch it.

See code here

4.5 Batch: Preparing a scoring script

See code here

4.6 MLOps Zoomcamp 4.6 - Batch scoring with Mage

No video - you already know how to do it:

• Connect to MLFlow
• Create a transformation block
• Get the model from the registry, apply it

Homework

More information here.

Notes

Did you take notes? Add them here:

• Notes on model deployment (+ creating a modeling package) by Ron M.
• Notes on Model Deployment using Google Cloud Platform, by M. Ayoub C.
• Week4: Notes on Model Deployment by Bhagabat
• Week 4: Deployment notes by Ayoub.B
• Week 4: Deployment notes by Waleed
• Week4: Deployment: Offline (Batch), Online (Web service /w MLflow, Streaming) by Hongfan (Amber)
• Week 4: Deployment Notes - Marcus
• Send a PR, add your notes above this line