Deploy ML Model in Production with FastAPI and Docker
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:
Project-Based Learning: Build 4 complete end-to-end ML applications including score prediction, wine quality classification, and iris species identification
Production-Level Skills: Learn industry best practices for API development, containerization, error handling, and latency optimization
Full-Stack Integration: Connect your ML models to both backend systems and user-friendly frontends
CI/CD Implementation: Establish automated testing and deployment pipelines used by professional development teams
Cloud Deployment Mastery: Deploy your solutions to multiple cloud providers with monitoring and scaling capabilities
Whether 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.
Instructors
- 4.2 Instructor Rating
- 27,567 Reviews
- 865,027 Students
- 79 Courses
Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for Coding, Finance & Excel.
We bring together both professional and educational experiences to create world-class training programs accessible to everyone.
Currently, we're focused on the next great revolution in computing: The Metaverse. Our ultimate objective is to train the next generation of talent so we can code & build the metaverse together!
- 4.2 Instructor Rating
- 5,097 Reviews
- 418,748 Students
- 48 Courses
We are a team of AI experts, engineers, researchers and enthusiastic professionals who are passionate about advancing artificial intelligence and teaching others. With over 50 years of combined experience building AI systems for a diverse range of industries, our goal is to share this collective knowledge with students who want to learn both the fundamentals and practical application of AI. Our courses are led by multiple instructors, allowing students to gain exposure to our varied areas of specialty - from machine learning to natural language processing to robotics and beyond. With a hands-on, project-driven approach grounded in ethics, we prepare students to apply AI to drive innovation. Join us if you share our excitement for AI and its vast potential!