Do you use open-source AI tools and want to stay ahead of the game in 2024?
A lot of new tools and technologies are coming out all the time in the area of AI. Because they are free and easy to use. That’s why open-source AI tools are becoming more and more common to AI developers.
Find out about the top 6 Open Source AI tools you need to know about for 2024. These tools will change the way you work with AI by giving you access to cutting-edge technologies and new apps.
Think about being able to use the most cutting-edge AI tools for free and without any restrictions. By streamlining your work and making your projects better, these tools will help you take your AI skills to the next level.
What is an Open Source AI Tool?
As the name suggests, an open-source AI tool is software or a platform that has been created and made available to everyone. Anyone can view, change, and use the source code. This lets people in the AI community work together and come up with new ideas since programmers can add to the tool, make it more useful, and change it to fit their needs.
A lot of the time, open-source AI tools are free to use. This makes AI technology more accessible to more people and encourages openness and community-driven development. Let’s find out the one that is most suitable for your AI development journey.
Related Read: 15+ Best AI Tools
6 Open Source AI Tools in 2024
1. TensorFlow
Google’s TensorFlow is an open-source machine-learning framework for developing and deploying cutting-edge machine-learning models. It all began as an internal Google Brain Team project to develop a deep-learning neural network in 2011. It later became an open-source AI tool in 2015.
Now it is widely used by data scientists, software developers, educators, and students due to its versatility and efficiency for training models. TensorFlow operates using data flow graphs with nodes representing mathematical operations and edges representing multidimensional arrays known as Tensors as nodes and edges respectively.
Due to its simplicity and ease as an Open source AI framework, TensorFlow is the most popular Open source AI tool for beginners.
Key Features of TensorFlow
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TensorFlow is flexible and provides modularization.
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Supports Javascript, Python, and C++
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Has high-level APIs like Keras to simplify model creation and training.
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Lets you visualize the graph's parts, making it easier to understand and debug.
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Trains neural networks on multiple GPUs. This parallelism is important for huge systems.
| Pros of TensorFlow | Cons of TensorFlow |
|---|---|
| TensorFlow supports mobile and IOT devices | Emphasizes numerical data over symbolic reasoning. |
| TensorFlow Processing Unit is faster than CPU and GPU for some specific task | It only supports NVIDIA and Python for GPU programming |
| TensorFlow library is always updated to ensure optimum performance |
Pricing of TensorFlow
TensorFlow is free and open source.
2. Pytorch
PyTorch is a powerful and easy-to-use deep-learning tool for Python. It is commonly used for computer vision, natural language processing, and speech recognition. It was released in 2016 by Facebook's AI research lab as an open-source Python-based tool for making AI/ML apps.
It works well with Python tools and supports GPU acceleration. As a result, training models and running experiments is quick and easy. A lot of researchers and developers use it to make quick prototypes of software and do studies on AI and deep learning.
Key Features of PyTorch
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Using TorchScript, PyTorch can switch between eager mode and graph mode
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You can deploy models on C++ using Pytorch
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Supports scalable distributed training for research and production.
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All the major public clouds support it for flexible development.
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PyTorch improves its API to handle common preprocessing and integration tasks for iOS and Android ML apps.
| Pros of PyTorch | Cons of PyTorch |
|---|---|
| PyTorch is well-known for its versatility and simplicity of use | You need third-party tools for visualization |
| It offers excellent support for GPUs, enabling accelerated computing | PyTorch is not perfect for larger data sets and slows down performance on large scale |
| PyTorch offers various deployment methods on the most popular cloud platforms |
Pricing of PyTorch
Pytorch is an Open source deep learning framework and you can download it for free.
Related Read: Best AI Tools for Developers
3. Keras
Keras is an advanced deep-learning API written in Python that provides an easy-to-use interface for creating and training neural networks. It was founded in 2015 by Google’s main contributor to TensorFlow name François Chollet. Keras assists developers by providing a high-level API, and run on popular frameworks like TensorFlow or PyTorch to perform tasks easily involving deep learning.
Key Features of Keras
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Keras is built around fundamental data structures, such as models and layers.
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It covers the ML workflow starting with data processing, then hyperparameter tuning, and finally deployment.
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Keras effortlessly works on GPUs and CPUs to effectively scale computation.
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You will get pre-defined layers, activation functions, loss functions, and optimizers in Keras.
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Offers fast experimentation for different architecture.
| Pros of Keras | Cons of Keras |
|---|---|
| Keras is best for beginners due to its easy UI | Doesn’t perform well with the complex architecture |
| Supports deployment on different types of devices | It lacks a dynamic chart creation feature |
| Allows users to build neural networks with minimal lines of code | Sometimes unusual errors can be seen |
Pricing of Keras
Keras is a free and Open source deep learning Python framework.
4. Rasa
While OpenAI offers generative AI models to build AI chatbots and AI assistants, it is not open source or free. But unlike OpenAI there is an alternative called Rasa. It is an Open-source AI tool that powers natural language (NLP) and natural language understanding (NLU) to create innovative chatbots and AI assistants.
Rasa was founded by Alex Weidauer in 2016 to provide developers with the tools they need to make conversational agents that fit their requirements.
Key Features of Rasa
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Rasa is very good at getting what users say because it understands natural language very well.
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Developers can customize Rasa chatbots to specific use cases by integrating them into different ML models.
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Rasa can keep track of the conversation's context, to intelligently respond to relevant answers.
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Rasa chatbots can be used on several platforms.
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Rasa pipelines let developers enhance chatbot intelligence by including machine learning models.
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Rasa encourages active learning by offering examples for human evaluation.
| Pros of Rasa | Cons of Rasa |
|---|---|
| Rasa Open Source lets you train models with user data, so it can be used with any language or topic. | Rasa is not ideal for beginners as you need additional NLP development knowledge |
| Rasa has a strong community and encourages developers to work together and come up with new ideas. | Rasa’s free options can’t take high traffic volume. You need to buy a paid plan for that |
| Developers can fine-tune their ML pipeline by following some primitives | Designed primarily for use with chatbots |
Pricing of Rasa
Rasa is open source and free but it comes with basic support and full access to the Rasa platform for $35k.
5. Acumos AI
Acumos AI is an open-source platform and framework that makes it easier to build, share, and use AI apps by standardizing the infrastructure stack and parts that AI models need to run. It supports Java, Python, R, and other supported toolkits or languages, as the platform is designed to be language-agnostic.
AT&T and Tech Mahindra worked together to make Acumos AI. The goal was to make an open-source AI tool under The Linux Foundation Deep Learning so that developers, entrepreneurs, and businesses could get more use out of AI.
Key Features of Acumos AI
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Standardized technology to make things easier to understand.
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Containerized rollout makes it easy to connect to the cloud and on-premises.
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Exports AI apps as Docker images for cloud or private use.
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A visual design tool that lets you drag and drop images.
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Data Broker to get data, train users on it, and store it.
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Integrates with various AI frameworks and tools.
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You don't need code knowledge to use the visible IDE to chain toolkits and models.
| Pros Acumos AI | Cons Acumos AI |
|---|---|
| Acumos AI is open-source and streamlines AI application development and deployment for everyone. | Acumos AI simplifies the integration of AI applications but beginners find the process challenging due to the cloud-based environment |
| Acumos AI user can share their pre-built AI applications in the Acumos AI marketplace | To deal with advanced programming language the user of Acumos AI needs to be an expert |
| Acumos AI is not tied to any specific run-time infrastructure, which may require users to adapt to different environments for deployment. |
Pricing of Acumos AI
Acumos AI is open source and free.
6. Apache OpenNLP
Apache OpenNLP is a set of tools for handling natural language processing that are based on machine learning. This free and open-source Java tool lets you get useful data from natural language sources like web pages and text files.
The Apache Software Foundation made it to help with different NLP tasks and give users a way to train and test models using a Command Line Interface (CLI). This library has models that are already set up for different languages, and users can download them and use them for jobs that depend on the language.
Key Features of Apache OpenNLP
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It can do standard NLP tasks like tokenization, POS tagging, named entity extraction, and more.
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A toolkit for machine learning that uses methods such as Naive Bayes, Maximum Entropy, and Perceptron.
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Offers pre-built models and annotated text resources for various languages.
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Gives you both an automated Java API and a command-line interface.
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The modular approach lets you build full NLP pipelines.
| Pros of Apache OpenNLP | Cons of Apache OpenNLP |
|---|---|
| Apache OpenNLP is an open-source project backed by the Apache Software Foundation | The pre-built models provided by Apache OpenNLP may need further training to suit the specific requirements of the user's application. |
| Apache OpenNLP API offers detailed documentation to get started | The development of Apache OpenNLP may be slow or stagnant |
| Easy to use API for beginners | Some models are missing in the documentation which need to be manually trained for their specific use cases. |
Pricing of Apache OpenNLP
Apache OpenNLP is open-source and free.
Advantages of Leveraging Open Source AI
In the past few years, artificial intelligence (AI) has come a long way. Open-source technologies are being used more and more in AI development, which is a big reason for this growth. Open-source AI has many benefits that help make AI technology more accessible and help it move forward.
1. Less expensive: Open-source AI systems are often free or cost less than private AI solutions, so using them can save you money.
2. Flexible and customizable: Open-source AI systems offer more customization and flexibility because users can change the code to fit their own needs.
3. Support from the community: Open-source AI projects often have a big group of developers and users who help each other, share information and work to make the system better.
4. Secure and transparent: The code for open-source AI systems is open, so users can see how the system works and find any possible security holes. This might help people trust the system more. identify any potential security vulnerabilities. This can help to increase trust in the system.
5. Avoid vendor lock-in: By using open-source AI, organizations can avoid being locked into a single vendor and have the flexibility to switch to a different solution if needed.
These are just the advantages of Open Source AI, but to know more about AI advantages you can check the benefits of Artificial intelligence.
Final Thoughts
Which open-source AI tools you will choose?
As of 2024, Open-source AI is changing how businesses grow and change. Overall, open-source AI tools have a bright future because they continue to spur innovation and make AI technology more accessible to everyone.
The 6 open source AI tools we chose have a big developer community to help with problems. They are great for building the roots of your next AI project because they bring together the smarts of developers around the world and some of the world's most cutting-edge research labs.
