Over the past decade, artificial intelligence (AI) has evolved from a futuristic concept into an integral part of our lives. Every day, AI is used, among others, to automate business processes, improve the accuracy of recommendation systems, better adapt content to recipients, and even in automatic and autonomous vehicles.
Artificial intelligence is constantly changing the way we work and integrate with new technologies. The widespread implementation of AI in an increasing number of products and services allows us to use available time and resources better and more effectively.
However, you should know that artificial intelligence has its own characteristics that should be taken into account when creating solutions supported by it. There are dedicated AI design frameworks on the market, such as TensorFlow, PyTorch and Keras, which are currently the basis for research and development in the field of artificial intelligence.
In this material, we discuss the most important issues regarding AI design frameworks and answer popular questions.
What is an AI programming framework?
An AI design framework is a specialized programming environment that provides a set of tools, libraries and interfaces that facilitate the creation, training and implementation of artificial intelligence models. Frameworks play a key role in accelerating AI development. These solutions enable engineers, scientists and developers to focus on algorithm design and problem solving rather than on the low-level aspects of programming.
Thus, dedicated frameworks adapted to work with artificial intelligence enable faster iteration and implementation of new ideas. They also facilitate the creation of advanced AI models and have a positive impact on standardization and work between teams, which is extremely important in such a dynamically developing field as artificial intelligence.
What are the most popular AI frameworks in 2024?
AI frameworks are constantly evolving, which is related to the dynamic development of artificial intelligence and its implementation into subsequent services and digital products.
Currently, the most popular AI programming frameworks include several solutions that are used by the largest companies in the world and have strong support from the community.
The most popular AI frameworks in 2024 include:
- TensorFlow
- PyTorch
- Keras
- JAX
- OpenAI Codex
Each of the above-mentioned frameworks is used to create projects related to artificial intelligence, but has different characteristics. The selection of an appropriate AI programming framework should be preceded by an analysis of the needs and requirements of a given project.
TensorFlow – why is it so popular among AI developers?
TensorFlow is an AI programming framework developed by Google – one of the largest technology giants in the world, which has also been interested in artificial intelligence solutions for many years.
The tool provided by Google is currently one of the most comprehensive and advanced frameworks for machine learning and deep learning related to the use of neural network models. This is why it is currently one of the most popular AI programming tools among developers around the world.
TensorFlow has an extensive library of tools for building AI models. It supports the TensorFlow Lite solution for building AI models for mobile devices, which is crucial in terms of the current direction of AI development. Another big advantage is strong integration with solutions collected in Google Cloud AI.
The Google framework currently has a very wide range of uses – from scientific research, through startup projects, to AI solutions produced by the largest technology companies on the market.
PyTorch – what features make it stand out from other frameworks?
PyTorch is also an AI programming framework created by one of the largest technology companies in the world. This solution is the result of many years of work and research on AI conducted by Facebook engineers, and is currently managed by the Linux Foundation.
The main feature that distinguishes PyTorch from other AI programming frameworks is its high flexibility and dynamic approach to building neural networks. This solution cannot be denied that it is highly intuitive.
The most important features that distinguish PyTorch from competing frameworks for creating AI projects include:
- Dynamic computational graphs
- Friendly debugging tools
- Support for distributed learning
- Good integration with the Python ecosystem
The support of a large and active community, which is complemented by very well-maintained documentation, is also invaluable among PyTorch users.
Active forums, discussion groups and online resources help you quickly solve encountered problems, and the documentation provided by the manufacturer contains detailed guides, tutorials and source code examples that make it easier to learn and understand the framework.
Which AI frameworks are best for beginners?
AI programming for beginners is a good way to start your adventure with creating source code. For people starting their adventure with artificial intelligence (AI) programming, choosing the right framework is crucial. A good framework for beginners should be easy to learn, well documented, and have an active support community.
People starting their adventure with artificial intelligence programming should pay special attention to two frameworks – Keras and Scikit-learn.
Keras is a high-level API based on TensorFlow from Google. The main advantage of this framework is its simplicity and intuitiveness, which strongly support the process of learning artificial intelligence programming.
Intuitive syntax and simplicity in defining models make the Keras framework an ideal solution for beginners. Keras is also supported by extensive documentation and numerous guides and source code examples. The whole thing supports rapid prototyping, which allows you to save time when testing models.
Scikit-learn, as its name suggests, is a framework designed specifically for learning artificial intelligence programming. It is also one of the most popular machine learning libraries in Python, which users appreciate for its simplicity, clarity, versatility and ease of use.
This framework is based on a simple and consistent syntax that makes it easier to learn the basics of machine learning and minimizes the risk of making mistakes. The whole thing is integrated with Python and works with other Python libraries.
Scikit-learn allows you to easily implement and test a wide range of algorithms, making it an ideal learning and prototyping tool.
What advanced AI frameworks are recommended for experienced developers?
Experienced programmers who intend to create complex AI projects can choose from many advanced and extensive AI frameworks, which are constantly developed and updated with new functions and capabilities.
Currently, the most popular framework used to build advanced artificial intelligence projects is TensorFlow from Google.
Advanced AI frameworks for experienced programmers also include:
- Deeplearning4j – an open-source distributed deep learning framework that is tailored to the needs of Java and Scala developers. Its compatibility with Hadoop and Apache Spark makes it a solid choice for processing large data sets.
- MXNet – an open source framework that is efficient and scalable. MXNet is popular in production environments and supports many programming languages, including Python, Julia and Scala. It also has interfaces for R and Go.
- JAX – an array-oriented numerical computing library that uses automatic differentiability and JIT (Just-In-Time) compilation to enable the study of efficient machine learning algorithms.
Advanced AI frameworks offer a wide range of features that enable work on complex projects, from research to production implementation of models. TensorFlow and PyTorch dominate this category thanks to their versatility and support for large projects. JAX and Apache MXNet are solutions offering high performance and scalability. The choice of the appropriate framework depends on the specifics of the project, performance requirements and the personal preferences of the programmer.
How to choose the right framework for your AI project?
Choosing the right framework for an artificial intelligence (AI) project depends on many factors, such as the specifics of the project, level of experience, hardware resources, and future development and implementation plans.
Below we present an example process of analyzing an AI project, which will allow you to choose the appropriate framework for its creation:
- Project goal – the main goal of the AI project should be specified. This could be creating machine learning models, processing data in real time, or building mobile applications using AI. The choice of framework should be tailored to the specific goals of the project.
- Type of application – it should be specified whether a web, mobile or desktop application will be created. The choice of framework depends on the application layer that will be supported (front-end or back-end).
- Performance and scalability – determine what performance and scalability requirements your project has. Some frameworks are more optimized for speed, while others offer more flexibility.
What should you pay attention to when choosing an AI framework?
When choosing an AI framework, you should pay attention not only to compliance with the requirements of a given project. It is also necessary to take into account the following issues:
- Knowledge of a programming language – it is worth choosing a framework that is compatible with the programming language with which we have experience. For example, developers proficient in Python should consider frameworks such as TensorFlow, PyTorch or Keras.
- Support and documentation – it is worth verifying how large the community is around a given framework. Together with thorough documentation, these elements will help solve potential problems.
- License – it is necessary to check what license terms apply to a given framework. There are open solutions and solutions based on commercial (paid) licenses on the market.
A thorough analysis of the above factors will allow you to choose a framework suitable for given applications, which will speed up work on your AI project.
The future of AI programming frameworks – what are the trends?
Artificial intelligence technology is currently one of the fastest-growing areas of new technology. Along with it, AI frameworks are also rapidly developing, which allow the use of the latest technological achievements.
The most important trends in the development of AI programming frameworks in the future include:
- Integration with cloud computing
- Process automation
- Development of ecosystems
- Optimization and efficience
- Better protection of the privacy of processed data
- Development of data virtualization tools
The future of AI programming frameworks is shaped by the dynamic development of technology and the growing needs of users. Key trends include cloud integration, ML process automation, the development of open-source ecosystems, performance optimization, advanced reinforced learning and meta-learning techniques, federated learning, and improving visualization tools and interactive work environments. Understanding these trends allows you to better prepare for future challenges and effectively use the latest developments in the field of AI.
FAQs:
Are there any free AI programming frameworks?
Yes. There are free AI frameworks on the market that are made available on an open-source basis. These include TensorFlow, PyTorch, Scikit-learn, Keras and JAX.
Which AI frameworks best support machine learning?
Most popular AI frameworks support machine learning. The most popular solutions that allow you to create, train and implement machine learning models include: TensorFlow, PyTorch, Keras and Scikit-learn.
What AI frameworks are most commonly used in the technology industry?
The technology industry most often uses AI frameworks that offer scalability, efficiency and support for advanced machine learning and deep learning techniques. These include TensorFlow, PyTorch, Scikit-learn and Keras.
