So you have built your machine learning model, so now what? In this video, I will share to you 4 approaches that you can use for deploying your machine learning model. I also share how I deploy my machine learning models in my own research work.

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⭕ Timeline
1:08 Obtaining the final machine learning model
1:25 Deploying the machine learning (ML) model
1:37 ML model as a data product
1:47 Four approaches to ML model deployment
1:52 Deployment format to use depends on the use case
2:30 Save ML model as objects
2:41 In Python, we can save as a pickle object
2:44 In R, we can save as a RDS object
3:01 Transfer ML-derived rules to a custom function, then apply this to make prediction
3:28 Create API to receive input and make prediction
3:59 Embed ML model inside a web application
4:04 In Python, popular web framework includes: Django, Flask and Dash
4:10 In R we have Dash and Shiny
4:21 Dash and Shiny are suitable for making data-driven dashboard
4:28 Shiny code can be deployed on your own web server or shinyapps.io

The idea for this video was suggested in a comment by seshendra vemuri

⭕ Playlist:
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✅ Data Science Toolbox: [ Ссылка ]
✅ Streamlit (Web App in Python): [ Ссылка ]
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✅ Google Colab Tips and Tricks: [ Ссылка ]
✅ Pandas Tips and Tricks: [ Ссылка ]
✅ Python Data Science Project: [ Ссылка ]
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