It is good to have coding knowledge or working experience in developing the software before jumping into the data science field. The consent of algorithms that can teach themselves to predict the output is amazing in itself.
However, if you do decide to start studying Machine learning and take help of video and courses then there is a chance that you will spend some weeks learning linear algebra and multivariable calculus before giving up.
The reason for this is that most introductory material for machine learning isn’t geared towards developers, it’s geared to ML researchers—and this is an issue for developers who just want to build products with machine learning.
Software with ML MODELS
I am assuming that you have been learning Deep Learning for a while now, you may have built some models using your Jupyter notebook or google Colab by doing a range of things, learning from image recognition to language translation. Now you may be thinking to tell your friends to try out your models but sending them a Jupyter notebook or Colab isn’t really what you had in mind. Now the question may arise How do you build a Web Application to deploy your Machine Learning models?
I will let you know of building a web app around our Machine Learning model for others to try it out. We will go through some Web programming techniques such as HTML and Flask, as well as deploying it on the Web on a Ubuntu server on DigitalOcean.
You will need some html knowledge to build the frontend.
Steps To Build Software With ML Models
1. Out first process to build the Software With ML Models will be to take the data.
2. After getting uploaded image from user our task is to provide the output of the data , so in this case you can see sample output.
3. For making this project you should be familiar with image recognition model.
4. After making your model you may need to deploy so that everyone can have access to this.
5. You need to setup your web server on digital ocean.
Link: https://m.do.co/c/e2093d3a5967
6. Once you register to this you will 100$ that you can use and you can start deploying your project, Dashboard will look like this
7. Click on plus icon to create a Droplet with its Linux based OS. after clicking on create droplet
8. After this use can choose pricing option and datacentre region as per your requirement
9. After all this you will get below page so you can ignore content for now and click on create
10. The project creation takes some time and after that you will see something like this
11. Now on mail you will get Ip address and other required information
12. Click on “Access console”. This will open up a window which will allow you to communicate to your Web server.
13. Now you can work on console and add the html file that you have made and for backend you need to install flask to run html file, enter below command:
nano index.htmlafter front end you may need to process backend details where you need below detail
- Load our Machine Learning model;
- Define what happens when he uploads the photo in the main homepage;and
- Apply our Machine Learning model to the image and show the user the results in a separate “prediction” page.
Summary
- Setting up on the cloud (DigitalOcean)
- Creating the Web App Backend using Flask
- Creating the Web App Frontend using HTML
For more detail step by step process you can visit: https://towardsdatascience.com/building-a-web-application-to-deploy-machine-learning-models-e224269c1331
Written By: Nikesh Maurya
Reviewed By: Krishna Heroor
If you are Interested In Machine Learning You Can Check Machine Learning Internship Program
Also Check Other Technical And Non Technical Internship Programs
