Ml Model Deployment With Fastapi And Streamlit

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.

Requirements

Basic understanding of Python such as data types, data structures, functions and classes.

Basic understanding of data science concepts like how to perform EDA and build a model.

Description

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.IntroductionIn 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 FastAPIThis 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 FastAPIYou 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 StreamlitIn 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 StreamlitIn 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.DeploymentThis section will show you how to deploy your Model API and Streamlit application using Render and Streamlit Cloud.WhatsApp AI Text-to-Image ChatbotThis 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 ProjectYou 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.

Overview

Section 1: Introduction

Lecture 1 Synopsis

Lecture 2 What is Model Deployment?

Lecture 3 Model Deployment Strategies

Lecture 4 Overview of FastAPI and Streamlit

Section 2: Building APIs with FastAPI

Lecture 5 Section Overview

Lecture 6 Path Parameters

Lecture 7 Query Parameters

Lecture 8 Request Body

Lecture 9 Input Validations

Lecture 10 Request Body Validations

Lecture 11 Forms and Files

Lecture 12 Response Status Codes

Lecture 13 Templates and Static Files

Lecture 14 Testing

Section 3: ML Models as an API with FastAPI

Lecture 15 Section Overview

Lecture 16 Project Structure

Lecture 17 Building the ML Model

Lecture 18 Model API Endpoints

Lecture 19 User Interface

Lecture 20 Testing

Section 4: Building Web Applications with Streamlit

Lecture 21 Section Overview

Lecture 22 Displaying Data

Lecture 23 Widgets

Lecture 24 Layout

Lecture 25 Caching

Lecture 26 Session State

Lecture 27 Paging

Section 5: Integrating FastAPI with Streamlit

Lecture 28 Section Overview

Lecture 29 Project Structure

Lecture 30 Building the Homepage

Lecture 31 Integrating API Endpoint with Streamlit

Section 6: Deployment

Lecture 32 Deploying FastAPI on Render

Lecture 33 Deploying Streamlit App on Streamlit

Section 7: WhatsApp AI Text-to-Image Chatbot

Lecture 34 Introduction

Lecture 35 Setting Up Project Directory

Lecture 36 Connecting to Vonage

Lecture 37 Connecting to OpenAI API

Lecture 38 Building the FastAPI Application

Section 8: Capstone Project

Section 9: BONUS Section

Lecture 39 Bonus Lecture

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.