Best Python Frameworks for Data Science, AI, & Web Development

Python Frameworks for Machine Learning

• Scikit-learn
  Scikit-learn is a free and open-source machine learning library built on NumPy, SciPy, and matplotlib. The library provides simple and efficient tools for predictive analysis, including model fitting, data preprocessing, model selection, model evaluation, and more. This makes Scikit ideal for building and deploying machine learning models in Python.
• PyTorch
  PyTorch is a machine learning framework based on the Torch library that was originally developed by Meta AI. It’s one of the most popular libraries for building and deploying deep learning models for computer vision and natural language processing use cases. Along with comprehensive built in features, PyTorch has a rich ecosystem of tools and libraries that extend its functionality.
• Keras
  Keras is an open-source deep learning library designed for fast experimentation. The library offers a user-friendly Python interface for working with deep neural networks, enabling users to rapidly implement and deploy deep learning models. Keras models can work with the JAX, PyTorch, and TensorFlow frameworks, benefiting from all of these large ecosystems.
• TensorFlow
  TensorFlow is an end-to-end machine learning framework with a focus on model training and inferencing. The framework is widely used in production for large-scale machine learning applications because it can be easily deployed across a variety of platforms and can run on multiple CPUs and GPUs. In addition, the TensorFlow Hub is a repository of reusable machine learning models that accelerate data science workflows.
• Natural Language Toolkit
  Natural Language Toolkit (NLTK) is a leading Python platform for natural language processing (NLP). This includes a suite of text-processing libraries and easy-to-use interfaces for over 50 corpora and lexical resources to simplify working with human language data. NLTK is ideal for sentiment analysis, chatbots, and other NLP use cases.

Python Frameworks for AI

• LangChain
  LangChain is a generative AI framework for integrating large language models (LLMs) into applications. This includes capabilities for building LLM applications, deploying them at scale, and managing their performance in production. LangChain integrates with many of the most popular LLM providers, including Open AI and Anthropic.
• OpenCV
  OpenCV is the largest open-source computer vision library, with over 2,500 algorithms for facial recognition, object tracking, image processing, and more. The library is highly optimized for real-time applications and can be deployed across a wide range of platforms. OpenCV is used extensively by leading companies like Google and Microsoft as well as small startups.

Python Frameworks for Web Development

• Django
  Django is an open-source web application framework for rapidly developing secure and scalable websites and applications. The framework is often called “batteries included” because it includes everything web developers need to get started out of the box. Django is ideal for building the backend components for large-scale web applications.
• Flask
  Flask is a micro web framework that was designed to be lightweight and only provides the essential components necessary for web applications. The backend framework is modular and relies on extensions to add functionality, such as authentication, database integrations, and REST APIs. The flexibility and extensibility of Flask makes it suitable for a variety of web projects.
• CherryPy
  CherryPy is a minimalist web application framework that’s designed for rapid web development. The object-oriented framework wraps the HTTP protocol to make it more Pythonic so developers can build web apps in a similar way they would build any other program. The lightweight nature of CherryPy makes it fast, reliable, and ideal for deploying to small servers and devices.
• Pyramid
  Pyramid is a full-stack web framework that allows developers to start small with a minimal web app and include additional features as the project grows. Pyramid strikes a balance between mega-frameworks that are highly opinionated and make decisions for developers, and micro-frameworks that lack the functionality required for larger applications.

Python Framework Management with Anaconda

There are many different Python libraries and frameworks for different use cases. However, handling large Python projects can become complicated without a comprehensive platform like Anaconda to streamline package management.

Anaconda is a leading platform for data science and machine learning, with a robust package manager and efficient environment management capabilities. The built-in package manager called Conda makes it easy to install, update, and manage popular Python libraries and frameworks. The platform also allows users to create isolated environments, each with its own set of packages to avoid dependency conflicts.

In addition, Anaconda provides an easily searchable repository of Python libraries and a Navigator interface for managing packages. Altogether, these features make it easier for data scientists to adopt and manage various Python libraries and frameworks for their projects.

Anaconda Business includes additional features like team collaboration, security, and scalability for large organizations. Anaconda’s Package Security Manager helps Python users remain compliant with regulations and manage their risks when working on any data science or AI projects. Anaconda ensures secure access to open-source libraries, addressing a critical risk for enterprises by protecting their workflows from potential vulnerabilities in third-party packages.

Request a demo to see if Anaconda is right for your AI and machine learning projects. Or, if you’re curious to experiment with Anaconda on your own, you can get started for free.