Top 5 Ways To Deploy Your Next Machine Learning Model! 😍🦾

Working towards building a new machine learning model? Don’t just stop there, go a step beyond and deploy it with one of the cool options below! 🙌 Now you know how to stand apart from the rest! 😉

1. Create a beautiful GUI using streamlit.io 🎨

Forget about HTML, CSS, and Javascript, this library helps you build responsive web apps using just Python! Dreamy stuff 🤓

https://streamlit.io/

2. Convert it into an API endpoint using Flassger  🗣

If you don’t fancy building a UI, this is for you. Flassger helps you create simple API endpoints using flask routes.

https://github.com/flasgger/flasgger

3. Deploy it on Heroku for free ☁️

If you already have an API or a webapp, you can use the heroku free tier to deploy it directly on the cloud for anyone to access. 

https://www.heroku.com/

 4. Go serverless using AWS Lambda ƛ

AWS Lambda provides an interface where you don’t have to worry about the infrastructure. You can just write your functions and they’ll act as endpoints. You can also set up triggers and automated actions! 🤩

https://docs.aws.amazon.com/lambda/

5. Dockerise it and deploy with AWS Fargate 🎁

Dockerization will give you a portable version of your application which will work in any environment! With Fargate, you can deploy your docker containers as tasks.

https://towardsdatascience.com/deploy-your-python-app-with-aws-fargate-tutorial-7a48535da586

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