Chapter 11
4. Model Deployment
4. Model Deployment
4.1 Three ways of deploying a model
4.2 Web-services: Deploying models with Flask and Docker
4.3 Web-services: Getting the models from the model registry (MLflow)
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.
4.5 Batch: Preparing a scoring script
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
- Cohort 2025| ML model deployment notes by Nitin Gupta
- week-4: Detailed notes on deployment,streaming module,notes,codes and homework by Muhammad Shifa
- Send a PR, add your notes above this line