ML Model Deployment: FastAPI, Streamlit & MLflow (Part 1)

Description

Support the Channel Here :- https://ko-fi.com/learnfree37902 CloudWays :- https://bit.ly/cloudways1301060 Rosehosting :- https://bit.ly/rosehosting2307 Hostinger :- https://bit.ly/hostinger4652 Contabo :- https://bit.ly/contabo100253646 In this video, part 1 of our ML Model Deployment series, we'll dive into deploying machine learning models using FastAPI, Streamlit, and tracking performance with MLflow! Learn how to take your trained models from research to production. We'll cover the basics of setting up a simple API with FastAPI, building a user-friendly interface with Streamlit, and then integrating MLflow for model monitoring and logging. This is the first video of a series where we will learn all about deploying ML models. We'll also explore other deployment frameworks like Gradio, Hugging Face, and Flask in later videos. We will also learn how to retrain the models with Airflow to achieve end-to-end MLOps. Specifically, in this video we will cover: * Setting up a basic FastAPI server * Creating a simple Streamlit application to interact with the API * Integrating MLflow for tracking model performance, logging metrics and parameters * End-to-End ML Model Deployment Stay tuned for future videos where we'll explore more advanced deployment strategies and tools, including Gradio, Hugging Face, Flask, model monitoring, and automated retraining with Airflow! #MLModelDeployment #MLOps #FastAPI #Streamlit #MLflow #MachineLearning #Deployment #Python #Tutorial #DataScience #ModelServing #Airflow #Gradio #HuggingFace #Flask