Udemy – 2025 Deploy ML Model in Production with FastAPI and Docker 2024-10

Description

2025 Deploy ML Model in Production with FastAPI and Docker, the ML model deployment training course in production with FastAPI and Docker has been published by Yudemy Academy. Discover the power of seamless ML model deployment with our comprehensive course, Deploying Production-Grade ML Models with FastAPI, AWS, Docker, and NGINX. This course is designed for data scientists, machine learning engineers, and cloud professionals who are ready to take their models from development to production. You’ll gain the skills needed to deploy, scale, and manage your machine learning models in real-world environments and ensure they’re robust, scalable, and secure. In today’s fast-paced technology landscape, the ability to deploy machine learning models in manufacturing is a highly sought-after skill. This course combines the latest technologies – FastAPI, AWS, Docker, NGINX and Streamlit – into a powerful learning journey. Whether you’re looking to advance your career or upgrade your skill set, this course provides everything you need to confidently deploy, scale, and manage production-grade ML models. By the end of this course, you will have the expertise to deploy machine learning models that are not only effective, but also scalable, secure, and production-ready in real-world environments. Join us and take the next step in your machine learning journey.

What you will learn

• Deploying Machine Learning Models with FastAPI: Learn how to build and deploy RESTful APIs to efficiently serve ML models.
• Cloud-based ML deployment with AWS: Get hands-on experience deploying, managing, and scaling ML models on AWS EC2 and S3.
• Automate ML operations with Boto3 and Python: Automate cloud tasks such as instance creation, data storage, and security configuration using Boto3.
• Containerize ML applications using Docker: Build and manage Docker containers to ensure consistent and scalable ML deployments across environments.
• Simple model inference with real-time APIs: Create high-performance APIs that deliver fast and accurate predictions for production-grade applications.
• Optimizing machine learning pipelines for production: design and implement end-to-end ML pipelines, from data acquisition to model deployment, using best practices.
• Implement a secure and scalable ML infrastructure: Learn to integrate security protocols and scalability features into your cloud-based ML deployment.
• Build interactive web applications with Streamlit: Build and deploy ML-based interactive web applications that are accessible and user-friendly.

Who is this course suitable for?

• Machine learning engineers who want to gain hands-on experience in setting up and configuring an end-to-end machine learning production pipeline.
• Data Scientists and Machine Learning Engineers: Professionals looking to advance their skills in deploying machine learning models in production environments using FastAPI, AWS, and Docker.
• Cloud Engineers and Developer Professionals: People who want to master cloud-based deployments, automate ML pipelines, and manage scalable infrastructure on AWS.
• Software developers and engineers: Developers interested in integrating machine learning models into applications and services, with a focus on API development and containerization.

Course specifications 2024 Deploy ML Model in Production with FastAPI and Docker

• Publisher: Udemy
• Lecturer: Laxmi Kant KGP Talkie
• Language: English
• Education level: all levels
• Training duration: 18 hours and 12 minutes

beginning of the course chapters on 2024/11

Course prerequisites

• Introductory knowledge of NLP
• Comfortable in Python, Keras, and TensorFlow 2
• Basic Elementary Mathematics

Pictures

Sample video of the course

download link

Download part 1 – 2 GB

Download part 2 – 2 GB

Download part 3 – 2 GB

Download part 4 – 2 GB

Download part 5 – 1.58 GB

File(s) password: www.downloadly.ir

File size

9.58 GB