Even if you are not directly involved in the field of artificial intelligence, the rapid and exciting developments such as ChatGPT, Llama, Midjourney, and Sora, to name just a few, are hard to miss. These tools allow us to do things that even a few years ago belonged to the realm of science fiction.

With the incredible pace of development in the field, we also see the emergence of multiple AI frameworks that practitioners and developers can use for their projects. Without a shred of doubt, the most significant benefit of such frameworks is abstraction. If we want to train a machine learning model, we do not need to read a paper introducing that model and then write potentially hundreds of lines of code ourselves. We can train such a model with a single function call and established frameworks, some of which are explored in this article. Additionally, since these are open-source, all contributions and changes are rigorously reviewed before being accepted and merged into the codebase. This ensures high quality and minimizes the chances of errors in implementing complex ML architectures.

Such a pace of development also results in some frameworks becoming obsolete not long after they were initially developed. For example, a few years ago, there were many more machine learning and deep learning frameworks (with some minor exceptions) that are not used anymore, such as Theano, Caffe, or Gluon/MXNet. Reasons they became obsolete include a lack of updates, better alternatives, or shifts in the field’s landscape.

This article will explore ten of the most popular AI frameworks available in Python.

What's Covered:

• Overview of top 10 AI frameworks and libraries in Python for 2024
• Key factors for choosing an AI framework
• Popular tools, including scikit-learn, XGBoost, TensorFlow, and PyTorch
• Emerging frameworks for large language models like LangChain and LlamaIndex
• Recommendations for framework selection based on AI task types

But before jumping into those, let’s answer the following question.

What to consider when choosing the AI framework?

Given the many AI frameworks available, choosing the appropriate one for your project can be daunting. Therefore, before exploring your options in-depth, it makes sense to consider several criteria that will help you with making an informed decision:

• Maturity: It's important to assess how long a framework has been under development and its current state. Semantic Versioning, with its format of MAJOR.MINOR.PATCH (e.g., 2.1.0) helps understand whether the framework is stable, under active development, or undergoing significant changes. Libraries such as LangChain and LlamaIndex are still in active development, and breaking changes are frequent with each minor and major release.
• Ease of learning: Some frameworks offer a high-level API that simplifies training machine learning models, while others have a steeper learning curve. You can evaluate the difficulty by going through some tutorials and coding examples.
• Community: The more widely adopted the framework, the larger its community. An active community can help whenever you encounter a problem or need clarification on how something works. Participating in the community is also beneficial for contributing and engaging with the open-source community.
• Performance: Frameworks vary in speed and scalability based on their implementation, which determines their suitability for distributed computing and utilization of hardware like multiple cores or GPUs.
• Flexibility: A flexible framework supports rapid prototyping and adaptation, which is crucial for meeting evolving research and application needs in the field.
• Open vs. closed-source: Open-source software offers transparency, community-driven innovation, and flexibility but may lack official support and raise some security concerns. Closed-source software provides dedicated support and security assurances but may limit customization and lead to vendor lock-in.
• Integration: Indicates how well a framework can be integrated into existing infrastructure and tooling. Some frameworks offer APIs compatible with leading approaches, such as LightGBM's scikit-learn API, allowing seamless integration into existing ML pipelines.

Now that we know what to look for let's explore the ten most popular AI libraries in Python.

Top 10 AI Frameworks and Libraries

Before exploring each framework, we'll categorize these tools by their primary use cases. This classification provides a quick reference for understanding each framework's core strengths while recognizing that many tools have broader applications. This overview will help you identify suitable tools for your AI projects, keeping in mind that the full capabilities of each framework may extend beyond these primary categories.

Scikit-learn

Description of Tool: The list could start with nothing but scikit-learn, one of the most widely used libraries for machine learning. It is the go-to choice for many practitioners due to its comprehensive functionalities covering all stages of a machine learning project—from data processing and manipulation to feature engineering, model training, and evaluation. Its robust and intuitive API allows users to get started quickly. Moreover, thanks to its user-friendly interface, experimenting with different models is often as simple as changing a single line of code.

Tool Application within AI: Scikit-learn is primarily used for traditional machine learning tasks such as classification, regression, clustering, and dimensionality reduction. As mentioned, its extensive end-to-end functionality enables entire projects to be effectively constructed using this single tool. If you need any functionality related to building ML models, it is probably already implemented in scikit-learn. Its ease of use and maturity make it suitable for academic research and business applications.

When to Leverage Tool: Scikit-learn is most appropriate for dealing with small—to medium-sized datasets and when you require robust and reliable implementations of ML algorithms. It is ideal for beginners and intermediate users due to its straightforward API and excellent documentation. It is also the preferred library for those needing to prototype and evaluate models quickly.

Tool Users: Data Scientist, Machine Learning Engineer, Business Analyst

Links to Documentation and Tutorials

• https://scikit-learn.org/stable/
• https://scikit-learn.org/stable/tutorial/index.html
• https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/\
• https://www.packtpub.com/en-us/product/python-machine-learning-9781789955750
• https://www.packtpub.com/en-us/product/machine-learning-with-pytorch-and-scikit-learn-9781801819312

XGBoost

Description of Tool: XGBoost (Extreme Gradient Boosting) is a highly efficient and scalable ML library for gradient boosting. XGBoost models are known for their impressive predictive performance. It can solve various data science problems, including regression, classification, and ranking, all quickly and accurately.

Tool Application within AI: XGBoost is widely used for tasks involving structured (tabular) data, such as fraud detection, risk modeling, and churn prediction. As it is known for its high predictive power, XGBoost (along with LightGBM, see the following framework) is a go-to tool for achieving top scores in ML competitions on platforms like Kaggle.

When to Leverage Tool: XGBoost is most suitable when performance and accuracy are critical for problems involving tabular data. Its scalability allows it to handle large datasets effectively while still training in a reasonable amount of time.

Tool Users: Data Scientist, Machine Learning Engineer, Data Analyst

Links to Documentation and Tutorials:

• https://xgboost.readthedocs.io/en/stable/
• https://xgboost.readthedocs.io/en/latest/tutorials/index.html

LightGBM

Description of Tool: LightGBM (Light Gradient Boosting Machine) is a highly efficient and fast gradient boosting framework designed by Microsoft. It is known for its high speed and accuracy. Its distinctive features include support for large-scale data, efficient parallel training, and support for training using GPUs and distributed systems.

Tool Application within AI: LightGBM is used in similar cases as XGBoost since these frameworks are very similar, with features often being introduced in one and then implemented in the other. Beyond tasks involving tabular data, LightGBM can also be used to solve some recommendation problems.

When to Leverage Tool: LightGBM is most appropriate for large tabular datasets that require high computational efficiency and predictive performance. Thanks to its low memory usage, it works exceptionally well in production environments and real-time applications.

Tool Users: Data Scientist, Machine Learning Engineer, Data Analyst

Links to Documentation and Tutorials:

• https://lightgbm.readthedocs.io/en/stable/
• https://lightgbm.readthedocs.io/en/latest/Features.html

TensorFlow

Description of Tool: TensorFlow is an open-source deep learning framework developed by Google. It provides a comprehensive ecosystem for building and deploying ML/DL models. Its low-level APIs offer flexibility and control while providing high-level APIs like Keras for simplified model building.

Tool Application within AI: Models built with TensorFlow can be applied to various tasks, particularly excelling with unstructured data such as images, audio, and text. As such, TensorFlow models are highly effective for image and speech recognition, object detection, natural language processing, and reinforcement learning. Due to its flexibility and customizability, TensorFlow is also extensively and successfully used in research and development.

When to Leverage Tool: TensorFlow is most appropriate for complex machine/deep learning tasks requiring high performance and scalability. It is ideal for research and production environments, especially when dealing with large, potentially unstructured datasets. It can also be deployed to various cloud, mobile, and IoT platforms.

Tool Users: Deep Learning Researcher, AI Engineer, Computer Vision Engineer

Links to Documentation and Tutorials:

• https://www.tensorflow.org/api_docs
• https://www.tensorflow.org/tutorials
• https://www.manning.com/books/deep-learning-with-python-second-edition

PyTorch

Description of Tool: PyTorch is an open-source library developed by Facebook's AI Research lab. Alongside TensorFlow, it is one of the most popular deep learning frameworks practitioners and researchers use. PyTorch is known for its flexibility and ease of use in building deep learning models, allowing for intuitive model design and efficient debugging.

Tool Application within AI: PyTorch is frequently used for tasks typically solved with deep learning, such as natural language processing, computer vision, and reinforcement learning. It works best when researchers or developers must experiment quickly with new ideas or complex models.

When to Leverage Tool: PyTorch is most appropriate when flexibility and development speed are priorities. It is ideal for research and production environments, especially when dealing with complex models requiring frequent adjustments and fine-grained control over their architecture. Practitioners who prefer a more Pythonic programming style often select PyTorch.

Tool Users: Deep Learning Researcher, NLP Engineer, AI Research Scientist

Links to Documentation and Tutorials:

• https://pytorch.org/docs/stable/index.html
• https://pytorch.org/tutorials/beginner/basics/intro.html
• https://www.packtpub.com/en-us/product/machine-learning-with-pytorch-and-scikit-learn-9781801819312
• https://www.manning.com/books/deep-learning-with-pytorch-second-edition

OpenCV

Description of Tool: OpenCV is an open-source computer vision library initially developed by Intel. It provides a comprehensive set of tools supporting many image and video processing tasks, such as object detection, image segmentation, and motion tracking. While written in C++, it has bindings for Python and other programming languages.

Tool Application within AI: OpenCV is frequently the go-to tool for tasks involving computer vision and image processing/augmentation. It is used in facial recognition, autonomous vehicles, and medical image analysis applications.

When to Leverage Tool: OpenCV is most appropriate for computer vision tasks that require image and video processing capabilities. It is ideal for both research and practical applications.

Tool Users: Computer Vision Engineer, Robotics Developer, Image Processing Specialist

Links to Documentation and Tutorials:

• https://docs.opencv.org/
• https://opencv.org/university/

Hugging Face

Description of Tool: Hugging Face is a company known for its NLP-focused library and transformers. It provides a platform for sharing models and datasets and showcasing interesting applications built using ML/DL. Hugging Face also offers libraries and tools for fine-tuning and deploying models in production.

Tool Application within AI: Hugging Face's tools and libraries are widely used for various text- and image-related tasks. They excel in text generation, sentiment analysis, named entity recognition, question answering, and chatbot development. Hugging Face models are particularly useful for their transfer learning capabilities, which allow users to achieve impressive results with minimal training data and training time.

When to Leverage Tool: Hugging Face tools are most appropriate when working on tasks that benefit from leveraging state-of-the-art pre-trained models. They are ideal for researchers, developers, and data scientists who need to prototype quickly, achieve high performance, or deploy models in production with minimal effort. Another reason to leverage Hugging Face is its vast repository of open-source models via the Transformers library.

Tool Users: Computer Vision Engineer, Robotics Developer, Image Processing Specialist

Links to Documentation and Tutorials:

• https://huggingface.co/docs
• https://huggingface.co/models
• https://huggingface.co/datasets
• https://github.com/huggingface/transformers?tab=readme-ov-file
• https://huggingface.co/learn

OpenAI

Description of Tool: OpenAI’s API provides developers easy access to state-of-the-art, pre-trained AI models developed by OpenAI (ChatGPT, Sora, Dall-E, etc.). This API empowers developers to integrate AI capabilities into their applications easily. To make it even easier, we can leverage a dedicated Python library that simplifies the process of interacting with the API.

Tool Application within AI: OpenAI’s API can be used for a multitude of tasks such as text, image, and audio (text-to-speech) generation, multi-turn chat, answering questions based on provided images (multimodality), transcribing audio from supported languages to text files, translation, and more. Additionally, the API can be used to fine-tune existing models to meet the specific needs of our projects.

When to Leverage the Tool: The OpenAI framework is ideal when we want access to state-of-the-art generative AI models from our application without the need to train or host the models ourselves. This framework is useful for both building complete products and prototyping using existing models before deciding to train our own.

Tool Users: AI Product Developer, Conversational AI Engineer, Software Engineer (AI Integration)

Links to Documentation and Tutorials:

• https://github.com/openai/openai-python
• https://github.com/openai/openai-python/blob/main/api.md

Langchain

Description of Tool: LangChain is an open-source framework that simplifies the process of building LLM-based applications. It can be used as a generic interface for interacting with LLMs. It can also help manage prompts, long-term memory, external datasets, and other agents for tasks that an LLM might struggle with, such as calculations or searches. LangChain’s modular approach allows users to compare different prompts and models with minimal code changes dynamically. Lastly, it can use multiple LLMs chained together. For example, one model can reason through a question while another constructs a response.

Tool Application within AI: LangChain can be used in startups and global enterprises thanks to its flexibility. When building an LLM-powered application (for example, a chatbot), LangChain is most likely the go-to framework.

When to Leverage the Tool: The tool can be used by researchers and practitioners alike. LangChain and its ecosystem of companion libraries simplify every stage of the LLM application lifecycle. After developing the core of your application with LangChain and its third-party integrations, you can productionize it using LangSmith. Additionally, you can use the tool to inspect, monitor, and evaluate what happens in each step of the chain. Lastly, any chain created with LangChain can be turned into an API with LangServe.

Tool Users: AI Application Developer, LLM Integration Specialist, Chatbot Developer

Links to Documentation and Tutorials:

• https://github.com/langchain-ai/langchain
• https://python.langchain.com/v0.2/docs/introduction/
• https://python.langchain.com/v0.1/docs/additional_resources/tutorials/

LlamaIndex

Description of Tool: LlamaIndex is an open-source framework that integrates LLMs with external data sources. Created with search and retrieval applications in mind, it simplifies the connection between LLMs and databases, documents (PDFs, Notion, etc.), APIs, and other data sources. Additionally, external data is indexed to allow the LLM to query it quickly and efficiently. Furthermore, you can also query the data yourself using natural language.

Tool Application within AI: LlamaIndex is used when LLMs need to access and process information from various external data sources. It is ideal for building Retrieval-Augmented Generation (RAG) systems. Specific use cases include developing intelligent chatbots with access to extensive knowledge bases (for example, company documentation) or building AI-driven data analysis tools.

When to Leverage Tool: LlamaIndex is the go-to tool for building RAG systems. It also works exceptionally well for products that summarize large documents or translate substantial chunks of text.

Tool Users: AI Application Developer, LLM Integration Specialist, Chatbot Developer

Links to Documentation and Tutorials:

• https://github.com/run-llama/llama_index
• https://docs.llamaindex.ai/en/stable/
• https://docs.llamaindex.ai/en/stable/getting_started/starter_example/
• https://www.datacamp.com/tutorial/llama-index-adding-personal-data-to-llms

Wrapping up

In this article, we have covered the ten most popular AI frameworks available for Python users. That is quite a lot to digest, so let’s wrap it up with a short overview of which library to use when:

• Tabular data: Start with scikit-learn, which offers end-to-end functionalities and dozens of ML models. If needed, you can then plug in compatible XGBoost or LightGBM models.
• Unstructured data and deep learning: Use either TensorFlow or PyTorch. It mostly boils down to personal preference, which framework you like more, but both can perform the same tasks.
• Working with images: For an out-of-the-box tool for working with images with some built-in ML capabilities, OpenCV is your tool. You can also use it for image augmentation before passing the extended dataset to custom DL models built with other frameworks.
• Utilizing pre-trained models and fine-tuning: Hugging Face contains dozens of state-of-the-art models that you can easily use in your applications, either directly or fine-tuning them. There are models for all the popular tasks related to text and images.
• Working with LLMs: Use LangChain to build an end-to-end application utilizing one or more LLMs. Use LlamaIndex to build a RAG system and allow the LLM to access external data sources efficiently.