Machine Learning Model Deployment with Streamlit

Machine Learning Model Deployment with Streamlit, available at $79.99, has an average rating of 4.6, with 44 lectures, based on 304 reviews, and has 2569 subscribers.

You will learn about Understand the core concepts and features of Streamlit Build interactive data-driven web applications to deploy your model Master the advanced features and integrations in Streamlit Apply the best practices and optimization techniques for Streamlit Connect your Streamlit app to data sources Deploy your Streamlit app for free This course is ideal for individuals who are Data scientists and machine learning engineers looking to deploy ML models and dashboards. It is particularly useful for Data scientists and machine learning engineers looking to deploy ML models and dashboards.

Enroll now: Machine Learning Model Deployment with Streamlit

Summary

Title: Machine Learning Model Deployment with Streamlit

Price: $79.99

Average Rating: 4.6

Number of Lectures: 44

Number of Published Lectures: 44

Number of Curriculum Items: 44

Number of Published Curriculum Objects: 44

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the core concepts and features of Streamlit
Build interactive data-driven web applications to deploy your model
Master the advanced features and integrations in Streamlit
Apply the best practices and optimization techniques for Streamlit
Connect your Streamlit app to data sources
Deploy your Streamlit app for free

Who Should Attend

Data scientists and machine learning engineers looking to deploy ML models and dashboards.

Target Audiences

Data scientists and machine learning engineers looking to deploy ML models and dashboards.

The complete course to deploy machine learning models using Streamlit. Build web applications powered by ML and AI and deploy them to share them with the world.

This course will take you from the basics to deploying scalable applications powered by machine learning. To put your knowledge to the test, I have designed more than six capstone projectswith full guided solutions.

This course covers:

Basics of Streamlit

Add interactive elements, like buttons, forms, sliders, input elements, etc.
Display charts
Customize the layout of your application
Capstone project: build an interactive dashboard

Caching

Performance enhancement with caching
Basic and advanced usage of caching
Capstone project: deploy a classification model

Session state management

Add more interactivity and boost performance with session state management
Basic and advanced usage of session state
Capstone project: deploy a regression model

Multipage applications

Build large apps with multiple pages
Capstone project: train and rank classification models

Authentication

Add a security layer with authentication
Add login/logout components
Advanced authentication with user management, reset password, etc.
Capstone project: deploy a clustering model for marketing

Connect to data sources

Connect to databases
Access data through APIs
Capstone project: Deploy a sales demand model

Deployment

Deploy a Streamlit app for free
Advanced deployment process with secrets management and environment variables

Course Curriculum

Chapter 1: Introduction to Streamlit

Lecture 1: Welcome!

Lecture 2: Installation and setup

Lecture 3: Overview of Streamlit and its features

Lecture 4: Creating a basic Streamlit app

Chapter 2: Streamlit fundamentals

Lecture 1: Text elements in Streamlit

Lecture 2: Data display elements

Lecture 3: Charting elements

Lecture 4: Input widgets – Part 1

Lecture 5: Input widgets – Part 2

Lecture 6: Forms in Streamlit

Lecture 7: Customize the layout

Lecture 8: Capstone project – Build an interactive dashboard

Lecture 9: Capstone project – Build an interactive dashboard – Solution

Chapter 3: Caching

Lecture 1: Basics of caching in Streamlit

Lecture 2: Code – Basics of caching

Lecture 3: Refactor our dashboard with caching

Lecture 4: Advanced caching in Streamlit

Lecture 5: Capstone project – Deploy a classification model with caching

Lecture 6: Improving our last capstone

Chapter 4: Session state management

Lecture 1: Basics of state mangement

Lecture 2: Code – State management

Lecture 3: Advanced state management

Lecture 4: Code – Advanced state management

Lecture 5: Build a temperature conversion calculator

Lecture 6: Capstone project – Deploy a regression model with state management

Chapter 5: Multipage applications

Lecture 1: Basics of multipage applications

Lecture 2: Code – Build your first multipage app

Lecture 3: Widget state mangement in multipage apps

Lecture 4: Code – Implement a workaround for multipage apps

Lecture 5: Capstone project – Train and rank different classification models

Chapter 6: Authentication

Lecture 1: Basic authentication

Lecture 2: Code – Basic authentication

Lecture 3: Streamlit-Authenticator

Lecture 4: Code – Streamlit-Authenticator

Lecture 5: Capstone project – Clustering for a marketing campaign

Chapter 7: Connect to data sources

Lecture 1: Connect to data sources

Lecture 2: Code – Connect to a database (Supabase)

Lecture 3: Code – Make API calls

Lecture 4: Capstone project – Deploy a demand forecasting model

Chapter 8: Deploy to production

Lecture 1: Deployment process

Lecture 2: Deploy a Streamlit app

Lecture 3: Advanced deployment concepts

Lecture 4: Deploy a Streamlit app with secrets

Lecture 5: Next steps

Instructors

Marco Peixeiro
Data Scientist and Instructor

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 20 votes
4 stars: 82 votes
5 stars: 201 votes

Frequently Asked Questions

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