ML Model Deployment & MLOps with FastAPI, Streamlit, MLflow

Post published:6 November, 2025
Post category:StudyBullet-22
Reading time:6 mins read

Deploy ML Models with Gradio, Hugging Face, Flask, monitor model performance with MLflow, and retrain model with Airflow
⏱️ Length: 4.6 total hours
👥 34 students

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Course Overview
- This comprehensive course is meticulously designed to bridge the critical gap between developing machine learning models and successfully deploying them into real-world production environments. It goes beyond mere model training, focusing squarely on the essential methodologies and tools required to operationalize your predictive analytics, making them accessible and useful to end-users or other applications.
- Learners will embark on a practical journey, exploring a diverse array of modern deployment frameworks that cater to various needs, from building robust APIs to creating interactive web applications. The curriculum emphasizes a hands-on approach, ensuring participants gain tangible experience in transforming raw ML models from notebooks into production-ready services.
- Furthermore, the course introduces fundamental MLOps (Machine Learning Operations) principles, crucial for maintaining, monitoring, and continuously improving deployed models. You’ll understand the importance of tracking experiments, managing model versions, and automating retraining pipelines to ensure your ML systems remain performant and relevant over time.
- Prepare to elevate your machine learning expertise by moving from experimental development to effective and sustainable deployment, equipping you with the full lifecycle management skills demanded by today’s data-driven industries.
Requirements / Prerequisites
- Basic Python Programming Proficiency: A foundational understanding of Python syntax, data structures, and object-oriented concepts is essential, as all course examples and deployment tasks will be implemented in Python.
- Fundamental Machine Learning Concepts: Familiarity with core machine learning principles, including supervised learning (classification, regression), model training, evaluation metrics (e.g., accuracy, precision, recall, RMSE), and basic model types (ee.g., decision trees, random forests) will allow you to grasp the model application context effectively.
- Working Knowledge of Data Science Libraries: Prior experience with popular Python libraries such as Pandas for data manipulation, NumPy for numerical operations, and Scikit-learn for building and training ML models will significantly aid in following the course content.
- Comfort with Command-Line Interface (CLI): Basic navigation and execution of commands in a terminal environment will be necessary for setting up development environments, running applications, and interacting with certain tools.
- Development Environment Setup: Access to a computer with Python 3.x and pip installed, capable of installing additional libraries and running web applications locally.
- No Prior Deployment or MLOps Experience Required: This course is structured to introduce these specific topics from the ground up, making it suitable for ML practitioners looking to expand into deployment and operations.
Skills Covered / Tools Used
- Deployment Frameworks & Platforms:
  - FastAPI: Master the creation of high-performance, asynchronous web APIs for serving machine learning models, ideal for robust backend services.
  - Streamlit: Learn to rapidly build interactive, shareable web applications and dashboards directly from Python scripts, providing intuitive interfaces for model interaction.
  - Flask: Gain expertise in developing lightweight, flexible web servers to expose ML model predictions as RESTful endpoints for integration with other systems.
  - Gradio: Discover how to generate quick and elegant web UIs for any ML model with minimal code, perfect for demonstrations and prototyping.
  - Hugging Face Spaces: Understand how to leverage this collaborative platform to deploy and share your machine learning models, particularly powerful for natural language processing and generative AI applications.
- MLOps Tools & Methodologies:
  - MLflow: Acquire skills in using MLflow for comprehensive experiment tracking, logging model parameters and metrics, managing model versions, and monitoring deployed model performance throughout its lifecycle.
  - Apache Airflow: Learn to orchestrate and automate complex data pipelines and model retraining workflows, ensuring your ML systems are continuously updated and robust.
  - API Design Principles: Develop an understanding of best practices for designing clear, efficient, and scalable APIs for machine learning services.
  - Containerization Fundamentals (Conceptual): Grasp the underlying principles of isolating application environments, which is crucial for reproducible and consistent deployments across different platforms.
  - Model Versioning & Registry: Implement strategies for managing different versions of your ML models, ensuring traceability and seamless updates.
- Applied Machine Learning:
  - Practical application of Scikit-learn models within a deployment context, reinforcing model development for production readiness.
  - Understanding the importance of preprocessing pipelines and feature engineering for robust model serving.
Benefits / Outcomes
- Operationalize Your Machine Learning Models: You will gain the tangible ability to take a raw, trained ML model and transform it into a functional, accessible web service or interactive application.
- Master Diverse Deployment Strategies: Acquire a versatile toolkit by gaining hands-on experience with multiple, industry-standard deployment frameworks (FastAPI, Streamlit, Flask, Gradio, Hugging Face Spaces), enabling you to choose the optimal solution for various project requirements.
- Implement Foundational MLOps Practices: Develop practical skills in experiment tracking, model versioning, performance monitoring with MLflow, and automated retraining with Airflow, establishing a strong foundation for managing the entire ML lifecycle in production.
- Build Robust & Scalable ML Solutions: Move beyond theoretical concepts to practical implementation, learning how to engineer resilient and scalable machine learning deployments that can handle real-world demands.
- Enhance Your ML Engineering Portfolio: Significantly boost your professional profile by showcasing concrete projects demonstrating your capability to deploy and manage ML models from development to production, a highly sought-after skill in the industry.
- Understand End-to-End ML Lifecycle: Gain a holistic perspective on the machine learning pipeline, from data ingestion and model training to deployment, monitoring, and continuous improvement.
- Prepare for ML Engineering Roles: Equip yourself with the practical knowledge and hands-on experience demanded by Machine Learning Engineer, MLOps Engineer, and Data Scientist roles focused on productionizing models.
PROS
- Wide Tool Coverage: Offers exposure to an impressive range of popular deployment frameworks and MLOps tools within a single course.
- Practical & Project-Oriented: Focuses heavily on hands-on application, enabling learners to build and deploy actual ML models.
- Essential Skill Development: Directly addresses the critical need for deployment and MLOps skills, bridging the gap between ML development and production.
- Introduction to Automation: Provides a valuable introduction to automating ML workflows with Airflow, a key component of modern MLOps.
CONS
- Limited Depth per Tool: Given the comprehensive scope and relatively short total duration (4.6 hours), the course may offer an introductory overview rather than in-depth mastery of each individual tool or advanced MLOps strategies.