Streamlit : Deploy Your Data & Ml App On The Web With Python

Create in a few hours a great interactive web application and deploy your data or AI model worldwide with Python!

What you'll learn

How to use Streamlit

Develop and deploy a Data application to share a Machine Learning models on the web

Scrape data in real time with an API (Yahoo Finance)

Using the Cloud with Streamlit Cloud

Create an attractive user interface (UI / UX)

Structure your Python program for web development

Know how to optimize a Streamlit application (Cache / Session / Form…)

Using Git and Github to version your code

Overcome the Jupyter Notebook and bring your Data project to life

Requirements

A basic knowledge of the Python programming language is required to better understand the concepts covered in this training. Simple knowledge is sufficient.

No web development and/or data engineering skills are required. All concepts are covered from the beginning.

No experience in the cloud is required. You will learn everything you need to know for the deployment/production part.

Description

Have you ever felt the frustration of having developed a great Machine Learning model on your Jupyter Notebook and never being able to test it against real-world use? That's the core value proposition of Streamlit: To be able to deploy your Data project on the web so that the whole world can use it through your own web application!Thus, all your Data projects will come to life! You will be able to : Share your beautiful image classifier so that other people can use your model by uploading their own images.Deploy the sentiment score of Elon Musk's latest tweets in real time with NLP.Or make interactive dashboards for your corporate teams with an authentication system to restrict access to only a few people.I developed this course after dozens of people contacted me to know how I developed a real-time train reservation web application used by more than 10 000 people. Because yes, you can use streamlit for any kind of application and not only for data / AI applications!In short, hundreds of use cases are possible with streamlit!The great thing about it is that all you need is some knowledge of Python.And that no skills in web development, data engineering or even cloud are necessary.This course is divided into 2 parts: An exercise part where we will see all the fundamentals of Streamlit, from connecting to a database system, through the creation of the interface and finally the part on deployment in the cloud!A second part dedicated to the training project: Development and production of a tracking and analysis application for S&P5O0 stocks, including the visualization of stock price evolution and the calculation of performance indicators. The data will be requested via an API.Take your data projects to the next level with Streamlit!Enjoy the training :) PS : This course is the english version of another french course on streamlit  that I put on udemy.

Overview

Section 1: Introduction

Lecture 1 Welcome message!

Lecture 2 Presentation of the training

Lecture 3 What is Streamlit ?

Lecture 4 What you will learn in this course ?

Section 2: Preparing your work environment

Lecture 5 Installation + Github directory download

Lecture 6 Code presentation

Lecture 7 Installation of the virtual environment

Section 3: The foundations of Streamlit

Lecture 8 Presentation

Lecture 9 Exercise part 1 - Streamlit fundamentals

Lecture 10 Exercise part 2 - Streamlit fundamentals

Lecture 11 Final project explanations

Lecture 12 Final Project part 1 - the fundamentalss

Section 4: Interaction with the user (UI / UX)

Lecture 13 Presentation

Lecture 14 Exercise Part 1 - Interaction

Lecture 15 Exercise Part 2 - Interaction

Lecture 16 Project Part 1 - Interaction

Lecture 17 Project Part 2 - Interaction

Section 5: Visualization with Streamlit

Lecture 18 Presentation

Lecture 19 Exercises - visualization

Lecture 20 Project - visualization

Section 6: Advanced features

Lecture 21 Presentation

Lecture 22 Form

Lecture 23 Session

Lecture 24 Cache

Section 7: Application deployment on the web with Streamlit Cloud

Lecture 25 Streamlit Cloud

Section 8: Conclusion

Lecture 26 Conclusion

People who are interested in Data and Python but are frustrated that they can never share their Machine Learning models around them!,Data Scientists in companies who want to share their Machine Learning work or dashboards internally for their collaborators.,Someone who has an idea for a web application project and wants to develop an MVP in a few hours!,All data scientists starting with the production of data applications