Data Science 101: Deploying your Machine Learning Model

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
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[@DataProfessor]

QUESTION OF THE DAY: How do you deploy your machine learning model? Or from the deployment approaches mentioned in this video, which approach do you like the best? Comments down below! 😃
💗Help support this TH-cam channel by hitting the Subscribe button, Like button and type #dataprofessor in the Comments section 👇

[@bkkz6769]

4 min and 1 min unnecessary lost in updates!

[@GiasoneP]

I just wanted to say thank you for your videos. Learning data science can sometimes be lonely. Your videos are inviting, clear, rich in information and extremely well made.

[@DataProfessor]

I am glad to hear that the contents are helpful and thanks for the kind words 😊

[@brontomind]

This is very clear and informative. At BrontoMind we are using two of these approaches to save the no-code ML model

[@Marc_Masters]

Minutes in and so much value

[@DataProfessor]

Thanks!

[@usmanafridi9668]

Thank you

[@tinanajafpour7214]

thank you for the video. really informative.

[@hassanrevel]

thanks a lot

[@dongilseo5727]

Thanks for sharing this video. Maybe it'd be a help for the watchers that Ainize is now providing ways to deploy AI models for free.

[@senjutsuwarrior]

Hi there, just came across your channel.. thank you for sharing.. wish you greater success~~

[@DataProfessor]

Thanks for the kind words!

[@chocol8milkman750]

video starts at 1:02

[@miraclechijioke1213]

Lmao

[@user-or7ji5hv8y]

Be great to see an example of deployment to Google Cloud

[@DataProfessor]

Thanks for the suggestion, will definitely consider this for future video.

[@jasjones82]

Thank you for this video Data Professor. Do you have a video where you actually show the steps in order to deploy an ML model?

[@DataProfessor]

Right, I’ll be putting that into my to-do list. Thanks for the suggestion 😃👍

[@jasjones82]

@@DataProfessor Thank you so much. I look forward to watching it.

[@mumbaikachallenge5526]

Amazing❤️

[@jakeb3055]

It may not be viable with FAANG companies, but there are still plenty of companies looking for qualified tech candidates

[@sainathkamble387]

Sir , is it necessary for a data scientist to learn deployment of ml models

[@debjyotibanerjee7750]

These days it is, if you are in a startup environment

[@vijaykumar-xk6lq]

Hi Professor, extraction of rules is possible for only decision tree algorithm? Or I can use it for any algorithm ?

[@nik3935]

Can u explain in brief by performing the method u mentioned in above video???

[@DataProfessor]

Hi Aniket, this video is about deploying the machine learning model. By deploying, it means to move the ML model to production phase which in our case would mean to package the ML model as a web application. In our research group we deploy our ML models as prediction web servers. Please check out one of our bioinformatics prediction webserver at codes.bio/osfp
A full paper describing the implementation of this webserver is available at jcheminf.biomedcentral.com/articles/10.1186/s13321-016-0185-8

[@docsmin8191]

@@DataProfessor How can we deploy it in the software? Like without putting it up on the web. Is this possible? If so, how can it be done?

[@MrBemnet1]

I didn't choose feature of my son.

[@VishalVerma-yt1su]

Hii, how can we deploy our Deep learning models into Hardwares ??

[@hussamcheema]

What are docker and kubernetes? How can we deploy ML model on docker or kubernetes?

[@muskaanvyas90]

These are tools which do certain task for deployment

[@wallnut8721]

cool

[@DataProfessor]

Thanks for watching 😃

[@alagappank1242]

Can we deploy using node js

[@DataProfessor]

Yes, but I recommend to deploy the Python model as an API first and then it is practically useable by any web dev language. Hope this helps.

[@vaishnavibollaboina6620]

Hello where does spark come into play , I mean whn to use it and y ?

[@samiagharib3796]

is it possible to deploy ml model as web service by google colab and flask ?

[@bhargav_ravinuthala6047]

Bla bla

[@spitfirelast8761]

Ok that's great and all.
But how do you deploy a ML into a website without using flask, django or dash? Basically what i'm asking is how do you deploy it from scratch withouth using any frameworks?