Top 5 Web Frameworks for Machine Learning Engineers to Learn in 2025

Machine learning engineers are no longer confined to designing and training models; deploying those models into user-facing systems is now an essential skill. Whether it's building dashboards, serving APIs, or crafting full-fledged web applications, the choice of framework significantly impacts development speed, scalability, and user experience.

Here's a comprehensive guide to the top 5 web frameworks I recommend machine learning engineers to consider picking up in 2025.

1. Streamlit

Streamlit stands out for its simplicity and speed, making it a favorite for prototyping. Built specifically for data science and machine learning, it allows engineers to:

• Build interactive dashboards with minimal code
• Use built-in widgets like sliders, dropdowns, and text inputs to gather user inputs
• Skip web development basics like HTML, CSS, or JavaScript

Advantages:

• Extremely fast setup, even for those without web development experience
• Perfect for showcasing machine learning models to stakeholders or clients
• Continuous development with an active community contributing new features

Drawbacks:

• Limited scalability for production use
• Not suitable for complex web applications or high-traffic environments

Recommendation: Streamlit is best for rapidly prototyping ideas, building internal tools, or presenting models interactively. However, for production-grade applications, you'll need a more robust framework.

2. FastAPI

FastAPI has revolutionized API development with its modern design, speed, and simplicity. It's particularly well-suited for deploying machine learning models as REST APIs. Key features include:

• Built-in data validation using Python type hints
• Asynchronous request handling for high-performance APIs
• Effortless integration with machine learning libraries like TensorFlow, PyTorch, or Scikit-learn

Advantages:

• High performance, thanks to asynchronous capabilities
• Excellent documentation and community support
• Ideal for deploying APIs that integrate ML models in microservices architecture

Drawbacks:

• Limited to back-end development; you'll need a separate front-end framework for UI
• Requires some familiarity with Python typing and async programming

Recommendation: If you're looking to serve machine learning models efficiently via APIs, FastAPI is your go-to framework. It's modern, scalable, and a skill that will remain highly relevant.

3. Django

Django is a mature, full-stack Python framework that excels at building robust, production-ready applications. It includes everything you need to create complex web systems, such as:

• Built-in authentication, ORM, and admin interfaces
• Django REST Framework (DRF) for building APIs
• A large ecosystem of plugins and libraries

Advantages:

• All-in-one solution for back-end and front-end development
• Highly secure, with features like CSRF protection and SQL injection prevention
• Perfect for enterprise-level applications where scalability and maintainability are critical

Drawbacks:

• Steep learning curve, especially for beginners
• Overkill for small or simple projects

Recommendation: Django is a long-term investment for machine learning engineers aiming to build robust, scalable applications. While the initial learning curve can be intimidating, tools like large language models (LLMs) make it easier than ever to overcome.

4. Flask

Flask is one of the simplest Python web frameworks, often used for lightweight applications and APIs. It offers:

• Minimalist design that lets you focus on the essentials
• Easy integration with libraries like NumPy, Pandas, and machine learning frameworks
• Complete control over your application's structure and components

Advantages:

• Great for learning the fundamentals of web development
• Flexible, with no enforced structure or dependencies
• Ideal for building small APIs or proof-of-concept projects

Drawbacks:

• Lack of built-in features means more manual setup
• Not suitable for large-scale applications or complex architectures

Recommendation: Flask is a great starting point for understanding web frameworks and deploying small-scale machine learning models. However, as your projects grow in complexity, you'll need to transition to more feature-rich frameworks like Django or FastAPI.

5. Node.js with Express

Node.js is a runtime for executing JavaScript on the server, and Express is its most popular framework. Together, they're a powerful choice for building scalable, high-performance web applications, especially for real-time interactions. Highlights include:

• Asynchronous and event-driven design for fast processing
• Extensive ecosystem of libraries and tools via npm
• Real-time data handling with WebSockets

Advantages:

• Ideal for real-time applications like chatbots or live dashboards
• Encourages learning JavaScript, which is a versatile and widely-used language
• Popular in full-stack development, making it easier to collaborate with front-end developers

Drawbacks:

• Requires familiarity with JavaScript, which may feel less intuitive for Python-focused ML engineers
• Less seamless integration with Python ML libraries compared to Python frameworks

Recommendation: Learning Node.js and Express adds a valuable tool to your skillset, especially for projects involving real-time features or when working with a JavaScript stack.

Why These Frameworks Matter

Each framework on this list serves a distinct purpose:

• Streamlit - Ideal for quick prototypes and internal tools
• FastAPI - Perfect for building efficient APIs to serve machine learning models
• Django - The best choice for scalable, production-grade web applications
• Flask - Great for learning or small projects
• Node.js with Express - Essential for real-time applications and JavaScript integration

Conclusion

For machine learning engineers, knowing which web framework to use can make or break your deployment strategy. Start with Streamlit to quickly bring your ideas to life, and then move to FastAPI or Django for building scalable, production-ready applications. If you're aiming to expand your stack or explore real-time applications, add Node.js with Express to your repertoire.