ML Model Deployment & MLOps with FastAPI, Streamlit, MLflow

ML模型部署与MLOps实战教程

本课程涵盖用FastAPI、Streamlit、Flask等部署ML模型，结合MLflow监控性能，并支持Airflow重训练。适合全级别学习者，4小时35分钟掌握模型上线全流程。

Published 11/2025
Created by Christ Raharja
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 21 Lectures ( 4h 35m ) | Size: 1.85 GB

Deploy ML Models with Gradio, Hugging Face, Flask, monitor model performance with MLflow, and retrain model with Airflow

What you’ll learn
Learn the basic fundamentals of machine learning model deployment and MLOps
Learn how to build earthquake detection model using Random Forest Classifier
Learn how to build flight ticket price prediction model using Decision Tree Regressor
Learn how to deploy machine learning model using Gradio
Learn how to deploy machine learning model using Streamlit
Learn how to deploy machine learning model on Hugging Face Space
Learn how to deploy machine learning model using Flask
Learn how to deploy machine learning model using FastAPI
Learn how to deploy machine learning model using Dash
Learn how to track and monitor model performance using MLflow
Learn how to package machine learning model using MLflow
Learn how to perform data augmentation
Learn how to retrain machine learning model using new data
Learn how to check and monitor data quality
Learn how to retrain machine learning model using Apache Airflow

Requirements
No previous experience in machine learning model deployment is required
Basic knowledge in Python