Learn ML deployment using FastAPI, Docker, CI/CD, and Cloud platforms
β±οΈ Length: 4.0 total hours
β 4.23/5 rating
π₯ 13,579 students
π May 2025 update
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- Course Title: Deploy ML Model In Production With FastAPI And Docker
- Course Caption: Learn ML deployment using FastAPI, Docker, CI/CD, and Cloud platforms. With a length of 4.0 total hours, this course boasts a strong 4.23/5 rating from 13,579 students, ensuring a proven learning experience. Stay current with the latest techniques, thanks to its May 2025 update.
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Course Overview
- This intensive, project-oriented course is meticulously designed to bridge the critical gap between developing machine learning models and successfully deploying them into real-world production environments. It addresses the common challenge faced by data scientists and ML engineers: transforming an experimental model from a Jupyter notebook into a robust, scalable, and accessible web service.
- You will embark on a comprehensive journey, learning how to containerize your ML applications using Docker, build high-performance asynchronous APIs with FastAPI, and establish continuous integration and deployment (CI/CD) pipelines. The curriculum culminates in deploying your fully operational ML service to a cloud platform, mimicking real-industry scenarios.
- By focusing on practical, hands-on application, this course empowers learners to not just understand theoretical concepts but to actively implement production-grade ML systems. Itβs an essential step for anyone looking to make their machine learning projects impactful beyond local development, ensuring models are not only accurate but also available, reliable, and maintainable in a live setting.
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Requirements / Prerequisites
- Basic Python Proficiency: A fundamental understanding of Python syntax, data structures, and object-oriented programming concepts is essential to follow along with the coding exercises.
- Familiarity with Machine Learning Fundamentals: You should have a foundational grasp of what machine learning models are, how they are trained (e.g., using libraries like scikit-learn, TensorFlow, or PyTorch), and basic concepts of model evaluation. No need to be an expert, but an understanding of model saving/loading is beneficial.
- Command-Line Interface (CLI) Basics: Comfort with navigating directories, executing commands, and interacting with your operating system via the terminal will be helpful.
- No Prior FastAPI, Docker, or CI/CD Experience: This course assumes no prior knowledge of these specific tools. All necessary concepts will be introduced from the ground up.
- Access to a Computer: A desktop or laptop with a stable internet connection and administrative rights to install software (Python, Docker Desktop, VS Code, etc.) is required.
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Skills Covered / Tools Used
- FastAPI for API Development: Master building blazing-fast, asynchronous Python web APIs. Learn to leverage Pydantic for robust data validation and serialization, implement dependency injection for cleaner code, and utilize FastAPI’s automatic interactive API documentation (Swagger UI/ReDoc).
- Docker for Containerization: Gain expertise in creating efficient Dockerfiles to package your ML application and its dependencies. Understand how to build, tag, and manage Docker images, run containers, and orchestrate multi-service applications using Docker Compose for local development.
- ML Model Serving Architectures: Learn best practices for packaging trained ML models (e.g., using joblib, pickle, or native library save formats) within your application, handling model loading efficiently, and designing API endpoints for real-time inference.
- RESTful API Design Principles: Develop a strong understanding of designing clean, scalable, and maintainable RESTful APIs for machine learning services, including request/response patterns, error handling, and versioning strategies.
- Continuous Integration/Continuous Deployment (CI/CD): Implement automated workflows for testing, building, and deploying your ML services. Explore concepts of CI/CD pipelines, potentially using tools like GitHub Actions or similar platforms to ensure rapid and reliable updates to your production environment.
- Cloud Deployment Strategies: Acquire practical skills in deploying containerized FastAPI applications to popular cloud platforms. This includes understanding the nuances of deploying to services like AWS EC2/ECS, Google Cloud Run, Azure Container Instances/Apps, or Heroku, and differentiating between serverless and VM-based approaches.
- Git and Version Control: Reinforce best practices for source code management using Git, crucial for collaborative development and integrating with CI/CD pipelines.
- Basic Monitoring & Logging (Conceptual): Understand the importance of monitoring the performance and health of deployed ML models and applications, along with implementing effective logging strategies for debugging and auditing.
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Benefits / Outcomes
- Production-Ready ML Engineer: Emerge with the practical skills to confidently take any trained ML model and deploy it as a robust, scalable, and maintainable web service ready for production use.
- API Development Proficiency: Master the art of building high-performance, well-documented APIs using FastAPI, a leading framework in the Python ecosystem for speed and developer experience.
- Containerization Expert: Gain invaluable experience with Docker, enabling you to package applications and their dependencies into portable containers, simplifying deployment and ensuring environment consistency.
- Automated Deployment Specialist: Understand and implement CI/CD pipelines, automating the entire process from code commit to deployment, leading to faster iterations and more reliable releases.
- Cloud Deployment Confidence: Be capable of deploying your containerized ML services to various cloud platforms, integrating your solutions into the broader cloud infrastructure.
- Enhanced Portfolio: Bolster your professional portfolio with hands-on projects demonstrating your ability to operationalize machine learning, significantly boosting your marketability for ML Engineering and MLOps roles.
- Bridge the ML Gap: Successfully bridge the gap between theoretical machine learning research and its practical application, transforming models into tangible business value.
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PROS
- Highly Practical Content: The course focuses heavily on hands-on implementation, ensuring learners gain actionable skills directly applicable to industry challenges.
- In-Demand Technologies: It teaches the use of FastAPI and Docker, two of the most popular and efficient tools currently used for ML model deployment, making skills immediately valuable.
- Concise and Efficient: At 4.0 hours, it’s designed to deliver core deployment competencies without excessive length, ideal for busy professionals.
- Strong Social Proof: A high rating from over 13,000 students signifies a well-received and effective learning experience.
- Up-to-Date Curriculum: The May 2025 update ensures the content reflects the latest tools, best practices, and features in the rapidly evolving MLOps landscape.
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CONS
- The concentrated nature of the course might necessitate self-study or further exploration for deep dives into highly advanced or niche topics within MLOps.
Learning Tracks: English,Development,Data Science
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