ML Model Deployment with FastAPI and Streamlit
Ridwan Adejumo Suleiman
4:35:14
Description
FastAPI and Streamlit Essentials for ML Model Deployment
What You'll Learn?
- Develop and deploy robust REST APIs with FastAPI for your ML models.
- Build interactive and user-friendly interfaces for interacting with your models using Streamlit.
- Deploy your ML applications on various platforms.
- Implement best practices for secure and scalable ML deployment.
- Showcase your ML models as interactive web applications for presentations or portfolio building.
Who is this for?
What You Need to Know?
More details
DescriptionWhy Deploy Models?
Machine learning models aren't just for show; they're meant to be used in the real world. By deploying your model, you can make it accessible to users who can benefit from its insights and predictions. There are several reasons why you should deploy your model:
1. User Interaction:
Machine learning is not just about building complex models; it's about providing value to users. Deploying your model allows users to interact with your insights through user-friendly interfaces like APIs or web apps, making your work more accessible and impactful.
2. Complex Applications:
Some machine learning models are designed to be used in complex applications such as AI voice assistants, video recommendations, and weather forecasting. Deploying your model enables these applications to leverage their predictions and provide valuable services to users.
3. Scalability and Efficiency:
Deploying your model on a server or cloud platform ensures scalability and efficiency. This allows you to handle a large number of requests simultaneously, ensuring that your model can serve multiple users without compromising performance.
4. Real-Time Predictions:
By deploying your model, you can enable real-time predictions. This is crucial for applications that require immediate responses, such as fraud detection systems or stock trading platforms. Deploying your model allows it to make predictions on the fly, providing users with up-to-date insights.
5. Continuous Improvement:
Deploying your model is not a one-time task. It involves continuous monitoring and improvement. By tracking your model's performance in real-world scenarios, you can identify areas for improvement and make the necessary adjustments to enhance its accuracy and effectiveness.
As you work through this course, consisting of eight sections, you will gain a comprehensive understanding of model deployment, including best practices, different deployment methods, and considerations for various use cases.
Each chapter is designed to achieve specific aims and objectives, equipping you with the knowledge and skills necessary to successfully deploy your machine learning models.
Introduction
In this section, you will delve into the concept of model deployment, exploring its significance and the diverse strategies employed in the process. We will provide a concise overview of FastAPI and Streamlit, shedding light on their distinct purposes and how they contribute to model deployment.
Building APIs with FastAPI
This section dives deep into building APIs with FastAPI, covering essential concepts like handling various parameters, receiving data inputs, and crafting user-friendly interfaces. You'll learn how to create APIs that accept and process diverse data from various sources while ensuring their quality and maintainability through effective testing practices. By the end, you'll be equipped to build robust and scalable APIs, confident in their ability to meet modern web requirements.
ML Models as an API with FastAPI
You will learn how to use FastAPI to create ML model APIs by building a weather model forecast API. You will build a forecast model from scratch and use it for prediction on an API endpoint.
Building Web Applications with Streamlit
In this section, you will be introduced to the basic components of a Streamlit application, including inputs, widgets, and layouts. You will also learn about caching and session management, two important features for building high-performance web applications.
Integrating FastAPI with Streamlit
In this section, you will build the user interface for the weather forecasting model API built in the previous section and also integrate the API with Streamlit.
Deployment
This section will show you how to deploy your Model API and Streamlit application using Render and Streamlit Cloud.
WhatsApp AI Text-to-Image Chatbot
This project will show you how to use your existing FastAPI knowledge with external tools such as Vonage and the DALL-E API to build a WhatsApp chatbot.
Capstone Project
You will showcase the skills you have learned in this course by building a full application that allows real estate agencies to predict house prices using various features.
Who this course is for:
- Data Scientists and Machine Learning Engineers with basic knowledge of Python and ML concepts.
- Developers interested in building and deploying web applications with machine learning capabilities.
- Anyone looking to enhance their ML project with interactive and user-friendly interfaces.
Why Deploy Models?
Machine learning models aren't just for show; they're meant to be used in the real world. By deploying your model, you can make it accessible to users who can benefit from its insights and predictions. There are several reasons why you should deploy your model:
1. User Interaction:
Machine learning is not just about building complex models; it's about providing value to users. Deploying your model allows users to interact with your insights through user-friendly interfaces like APIs or web apps, making your work more accessible and impactful.
2. Complex Applications:
Some machine learning models are designed to be used in complex applications such as AI voice assistants, video recommendations, and weather forecasting. Deploying your model enables these applications to leverage their predictions and provide valuable services to users.
3. Scalability and Efficiency:
Deploying your model on a server or cloud platform ensures scalability and efficiency. This allows you to handle a large number of requests simultaneously, ensuring that your model can serve multiple users without compromising performance.
4. Real-Time Predictions:
By deploying your model, you can enable real-time predictions. This is crucial for applications that require immediate responses, such as fraud detection systems or stock trading platforms. Deploying your model allows it to make predictions on the fly, providing users with up-to-date insights.
5. Continuous Improvement:
Deploying your model is not a one-time task. It involves continuous monitoring and improvement. By tracking your model's performance in real-world scenarios, you can identify areas for improvement and make the necessary adjustments to enhance its accuracy and effectiveness.
As you work through this course, consisting of eight sections, you will gain a comprehensive understanding of model deployment, including best practices, different deployment methods, and considerations for various use cases.
Each chapter is designed to achieve specific aims and objectives, equipping you with the knowledge and skills necessary to successfully deploy your machine learning models.
Introduction
In this section, you will delve into the concept of model deployment, exploring its significance and the diverse strategies employed in the process. We will provide a concise overview of FastAPI and Streamlit, shedding light on their distinct purposes and how they contribute to model deployment.
Building APIs with FastAPI
This section dives deep into building APIs with FastAPI, covering essential concepts like handling various parameters, receiving data inputs, and crafting user-friendly interfaces. You'll learn how to create APIs that accept and process diverse data from various sources while ensuring their quality and maintainability through effective testing practices. By the end, you'll be equipped to build robust and scalable APIs, confident in their ability to meet modern web requirements.
ML Models as an API with FastAPI
You will learn how to use FastAPI to create ML model APIs by building a weather model forecast API. You will build a forecast model from scratch and use it for prediction on an API endpoint.
Building Web Applications with Streamlit
In this section, you will be introduced to the basic components of a Streamlit application, including inputs, widgets, and layouts. You will also learn about caching and session management, two important features for building high-performance web applications.
Integrating FastAPI with Streamlit
In this section, you will build the user interface for the weather forecasting model API built in the previous section and also integrate the API with Streamlit.
Deployment
This section will show you how to deploy your Model API and Streamlit application using Render and Streamlit Cloud.
WhatsApp AI Text-to-Image Chatbot
This project will show you how to use your existing FastAPI knowledge with external tools such as Vonage and the DALL-E API to build a WhatsApp chatbot.
Capstone Project
You will showcase the skills you have learned in this course by building a full application that allows real estate agencies to predict house prices using various features.
Who this course is for:
- Data Scientists and Machine Learning Engineers with basic knowledge of Python and ML concepts.
- Developers interested in building and deploying web applications with machine learning capabilities.
- Anyone looking to enhance their ML project with interactive and user-friendly interfaces.
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Ridwan Adejumo Suleiman
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 37
- duration 4:35:14
- Release Date 2024/05/18
