In this guide, you will learn how to deploy a machine learning model as an API using FastAPI. We will create an API that predicts the species of a penguin based on its bill length and flipper length.PrerequisitesBasic knowledge of PythonPython installed on your system (preferably version 3.7 or higher)Familiarity with machine learning concepts (optional)Step 1: Set Up Your EnvironmentCreate a Project DirectoryOpen your terminal and create a new directory for your project:Set Up a Virtual EnvironmentOn windows use: venvScriptsactivateInstall Required PackagesStep 2: Prepare Your Machine Learning ModelDownload Dataset here.Create a Python Script for the ModelStep 3: Create the FastAPI ApplicationCreate the Main Application FileStep 4: Run Your FastAPI ApplicationRun the ApplicationAccess the APIStep 5: Test Your APIUse Swagger UIConclusionCongratulations! You have successfully deployed a machine learning API using FastAPI. This guide covered:Setting up your environment.Preparing a machine learning model.Creating a FastAPI application.Running and testing your API.Next StepsExplore more advanced features of FastAPI like authentication and database integration.Experiment with different machine learning models and datasets.Consider containerizing your application using Docker for easier deployment.Feel free to reach out if you have any questions or need further assistance! NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces the Kolmogorov-Test: A Compression-as-Intelligence Benchmark for Evaluating Code-Generating Language ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces PLAN-AND-ACT: A Modular Framework for Long-Horizon Planning in Web-Based Language AgentsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces GRPO-based Open-RS: A Low-Cost Reinforcement Learning Framework to Enhance Reasoning in Small Language ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/Qwen Releases the Qwen2.5-VL-32B-Instruct: A 32B Parameter VLM that Surpasses Qwen2.5-VL-72B and Other Models like GPT-4o Mini