Deploy Ml Model In Production With Fastapi And Docker
Published 3/2025
Created by Meta Brains,Skool of AI
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 57 Lectures ( 3h 57m ) | Size: 1.7 GB
Learn ML deployment using FastAPI, Docker, CI/CD, and Cloud platforms
What you'll learn
Deploy machine learning models in production using FastAPI and Docker.
Create APIs for ML models using FastAPI with optimized endpoints.
Containerize ML applications with Docker for scalable deployments.
Set up CI/CD pipelines for automated deployment and testing.
Train, evaluate, and save ML models, focusing on real-world datasets.
Deploy ML models to cloud platforms like Heroku and Microsoft Azure.
Build and integrate a simple frontend for ML model APIs.
Implement logging, error handling, and request handling in APIs.
Requirements
Basic knowledge of Python programming.
Familiarity with machine learning concepts and workflows.
A computer with internet access for software setup.
Willingness to learn and experiment with new tools like Docker and FastAPI.
Description
Stop building models that live and die in notebooks. It's time your ML creations actually see the light of day.Transform your machine learning projects from academic exercises to production-ready applications with this comprehensive, hands-on course. Master the entire ML deployment pipeline using industry-standard tools that employers are actively seeking.In this practical journey, you'll build real-world ML systems that deliver actual business value. Starting with fundamental ML concepts, you'll quickly progress to crafting robust APIs with FastAPI, containerizing applications with Docker, and deploying scalable solutions across multiple cloud platforms including Heroku and Microsoft Azure.What sets this course apart
roject-Based Learning: Build 4 complete end-to-end ML applications including score prediction, wine quality classification, and iris species identificationProduction-Level Skills: Learn industry best practices for API development, containerization, error handling, and latency optimizationFull-Stack Integration: Connect your ML models to both backend systems and user-friendly frontendsCI/CD Implementation: Establish automated testing and deployment pipelines used by professional development teamsCloud Deployment Mastery: Deploy your solutions to multiple cloud providers with monitoring and scaling capabilitiesWhether you're a data scientist looking to operationalize your models or a developer wanting to integrate ML into production applications, this course provides the missing link between experimental machine learning and deploying systems that create real business impact.By completion, you'll have a portfolio of deployed ML applications and the confidence to implement end-to-end ML systems that showcase your capabilities to potential employers.Don't just be another data scientist with models trapped on your hard drive. Become the invaluable engineer who makes ML work in the real world.Who this course is for
Aspiring data scientists seeking to learn model deployment.
Machine learning engineers aiming to enhance deployment skills.
Software developers interested in integrating ML into applications.
Tech enthusiasts curious about Docker, FastAPI, and cloud deployments.
Professionals transitioning into MLops or AI engineering roles.
Students with basic Python knowledge looking to build end-to-end ML projects.
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