Open source AI tools: Pros and cons, types, and top 10 projects

About open source AI

Open source AI technology benefits from collective innovation and the expertise of a global community. Many advancements in AI, such as new machine learning frameworks, architectures and algorithms, as well as advancements in large language models (LLMs), originate from open source projects.

AI technologies are characterized by their ability to adapt and evolve rapidly due to collaborative contributions. Open source AI provides a platform for innovation, granting everyone from smaller organizations and individual developers to large enterprises access to powerful, low-cost technology platforms.

The open source AI ecosystem

The open source ecosystem has seen rapid growth and evolution, particularly in AI. A recent report from GitHub highlights several key trends reshaping the developer experience and the broader impact of open source technologies:

• Rise of generative AI: Generative AI projects have surged recently, with many developers experimenting with foundation models from major AI players like OpenAI, as well as new open source models like Meta LLaMA and Mistral. Open source generative Multiple AI projects have entered the top 10 most popular open source projects by contributor count.
• Cloud-native applications: The adoption of cloud-native technologies has increased, with more developers using Git-based infrastructure as code (IaC) workflows, Dockerfiles, containers, and other cloud-native tools. This trend underscores the importance of open source in supporting scalable and standardized cloud deployments.
• New contributors and growing communities: The year 2023 saw the largest number of first-time open source contributors, with generative AI projects attracting many of these new contributors. Commercially-backed open source projects continue to capture the largest share of contributions, but individual developers also play a critical role in driving innovation.
• Private projects: The number of private projects on GitHub increased by 38% year over year, demonstrating the growing use of open source AI tools in proprietary settings, for example fine-tuning and customization of LLMs.

Pros and cons of open source AI technology

Open source AI offers several benefits for developers and organizations:

• Adaptation to specific use cases: Open source AI tools typically allow more customization, allowing organizations to develop solutions tailored to their specific needs. Many open source developers share their specific customizations, allowing others in the community to benefit from their work.
• Community engagement: An active and engaged community is a key driver in open source development. Community involvement means continuous improvement, regular updates, and the swift resolution of issues. Contributors from around the world collaborate on coding, debugging, and optimizing AI technologies, creating a support network for both novice and experienced users.
• Transparency: Open source AI promotes transparency by allowing users to inspect and modify the source code. This level of access ensures that AI systems are accountable and can be scrutinized for issues such as bias or ethical concerns. Transparent AI models build trust among users and stakeholders, as they can understand how decisions are made and ensure that the algorithms adhere to ethical standards.
• Iterative improvement: As multiple contributors review and enhance the codebase, the software evolves continuously. This is especially important given the breakneck pace of innovation in AI in recent years. Open source projects benefit from a diverse pool of contributors who bring different perspectives and expertise, driving rapid evolution and refinement.
• Vendor neutrality: Open source AI provides freedom from vendor lock-in, offering organizations the flexibility to choose and switch between tools and platforms without incurring significant costs or disruptions. This allows organizations to maintain control over their technology stack and avoid dependency on a single vendor’s ecosystem or pricing models.

Open source AI tools also have some important limitations:

• Lack of control: Since projects are developed by a broad community, there is often no single entity responsible for guiding the direction of the project. This can result in fragmented development, where updates or critical bug fixes might be delayed, or where changes are implemented that don’t align with specific user needs.
• Intellectual property risks: Open source licenses vary in their terms, and some impose requirements such as making derivative works open to the public. Organizations that incorporate open source AI into proprietary solutions need to carefully review the license terms to avoid inadvertently violating them. Additionally, because the code is publicly available, there is a risk that competitors may use it to replicate or undercut proprietary solutions.
• Resource demands: Open source AI systems often require significant internal expertise and resources to deploy, manage, and maintain. Unlike commercial AI products that come with professional support and infrastructure, open source projects typically rely on community-based support, which may not always be timely or adequate for mission-critical applications. Organizations must have skilled personnel to implement and adapt these tools, as well as the infrastructure to support them.

Common types of open source AI solutions

Open source AI includes several types of solutions. Here are some of the most common ones.
 

Data platforms

Open source data platforms provide the foundation for data storage, management, and processing. Solutions like Apache Hadoop® and Apache Spark™ enable large-scale data operations, supporting the efficient handling of vast datasets required for training AI models. These platforms offer scalability, making them suitable for various AI applications across industries.

These data platforms usually integrate with other open source tools, creating an ecosystem that supports end-to-end AI workflows. Their modular architecture allows organizations to tailor solutions to their needs.
 

Databases

Databases provide repositories for structured and unstructured data. Tools like PostgreSQL® offer reliable and scalable database solutions that support the data management needs of AI applications. In addition, there are several open source vector databases available (including Apache Cassandra® 5.0), which support new AI technologies like LLM and RAG.

The open source nature of these databases ensures that they are continuously updated and improved by a global community of contributors. This collective effort results in stable and secure database solutions, which are typically compatible with popular data processing and analysis tools.
 

Data processing and analysis tools

Data processing and analysis tools are vital for transforming raw data into actionable insights. Open source solutions like Apache Kafka®, Apache Flink®, and ELK Stack provide capabilities for real-time data processing, simplifying the preparation of data for AI model training. These tools support a range of data types and sources, allowing organizations to use diverse datasets.

With open source data processing tools, organizations can perform complex data operations such as filtering, aggregation, and enrichment. These tools enable the rapid handling of large volumes of data, ensuring that AI models are trained on high-quality, relevant information.
 

Data catalog tools

Data catalog tools are useful for organizing and managing data assets within an organization. These tools enable easy data discovery, lineage tracking, and governance, ensuring that data is accessible, reliable, and well-documented.

With data catalog tools, organizations can improve their data management practices, ensuring data quality and compliance. These tools support collaboration across teams, as users can quickly locate and understand the data they need.
 

Data visualization tools

Data visualization tools are useful for interpreting and presenting complex datasets in an understandable manner. Open source solutions like Grafana, D3.js, and Plotly offer visualization capabilities, allowing users to create interactive and insightful visual representations of their data.

These tools support a range of visualization types, from simple charts and graphs to intricate, multi-dimensional displays. Open source visualization tools are typically customizable, enabling users to tailor visualizations to their needs and preferences.
 

Workflow and orchestration tools

Workflow and orchestration tools simplify the management and automation of data workflows, enabling the efficient coordination of various tasks in an AI pipeline. Open source solutions like Apache Airflow®, MLFlow, Luigi, and Prefect offer frameworks for defining, scheduling, and monitoring machine learning workflows. For Kubernetes environments, Kubeflow can be used to manage ML workflows.

These tools support complex task dependencies and ensure that data processing steps occur in the correct sequence, which is crucial for maintaining data integrity and consistency. They also provide capabilities for error handling, retry mechanisms, and alerting, which are essential for operational reliability.
 

Machine learning frameworks

Machine learning frameworks provide tools and libraries necessary for building and training AI models. Open source frameworks like TensorFlow, PyTorch, and Keras are widely used due to their flexibility, scalability, and community support. These frameworks offer a range of pre-built models, optimization algorithms, and utilities that simplify the AI development process.

The ecosystems surrounding these frameworks encourage innovation and improvement. Developers can access a wealth of resources, including documentation, tutorials, and open source contributions, to improve their machine learning projects.
 

Computer vision libraries

Computer vision libraries are specialized tools for processing and analyzing visual data. Open source libraries like OpenCV, Dlib, and SimpleCV provide a rich set of functions for tasks such as image recognition, object detection, and facial recognition. These libraries are optimized for performance and can handle complex image processing tasks.

By using open source computer vision libraries, developers can access pre-trained models and algorithms, accelerating the development of their projects. These libraries integrate with other AI tools and frameworks, enabling the creation of end-to-end computer vision solutions. The contributions from the open source community ensure that these libraries remain current.
 

Large Language Models (LLMs)

LLMs are advanced AI systems based on transformer neural networks, optimized for processing and generating human language. Powerful open source LLMs are now available, including Meta LLaMA, Mistral, and Falcon, each using billions of parameters and pre-trained on extensive datasets, enabling them to perform complex language tasks.

Proprietary LLMs offer state of the art capabilities, but come with limitations such as restricted transparency, potential data security concerns, costly licensing fees, and limited ability to deploy them on-premises. Open-source LLMs provide a transparent and cost-effective alternative. They allow organizations to retain full control over their data, mitigating security risks associated with third-party providers.
 

Top open source AI projects

1. TensorFlow

TensorFlow is an open source machine learning framework developed by Google. It provides an ecosystem for building, training, and deploying AI models. TensorFlow supports a variety of tasks, including neural network training, data preprocessing, and model optimization.

The TensorFlow community is highly active, contributing to documentation, tutorials, and pre-trained models. TensorFlow’s tooling, including TensorFlow Lite for mobile and embedded devices and TensorFlow.js for web-based applications, extends its utility across platforms.

Repo: https://github.com/tensorflow/tensorflow
GitHub stars: 180K+
Contributors: ~3550
License: Apache License 2.0

Source: TensorFlow

 

2. PyTorch

PyTorch, developed by Facebook’s AI Research lab, is another prominent open-source machine learning framework. Known for its dynamic computation graph and intuitive design, PyTorch has gained popularity among researchers and practitioners. It supports deep learning algorithms and provides tools for building and training complex neural networks efficiently.

The PyTorch ecosystem includes libraries like torchvision for computer vision tasks and torchaudio for audio processing. PyTorch’s community-driven nature ensures continuous improvement and the availability of numerous pre-trained models and extensions.

Repo: https://github.com/pytorch/pytorch
GitHub stars: 80K+
Contributors: ~3400
License: Modified BSD license

Source: PyTorch

 

3. Keras

Keras is an open source neural network library written in Python, enabling easy and fast prototyping of deep learning models. It is user-friendly and modular, allowing developers to build and train models with minimal code. Keras runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit, providing a high-level API for deep learning tasks.

The simplicity and flexibility of Keras make it an appropriate choice for both beginners and experienced practitioners. Keras comes with a range of pre-built layers, optimizers, and loss functions, enabling rapid model development and experimentation.

Repo: https://github.com/keras-team/keras
GitHub stars: 60K+
Contributors: ~1250
License: Apache License 2.0

 

4. OpenCV</h3

OpenCV (Open Source Computer Vision Library) provides a set of functions for image and video processing, enabling tasks such as object detection, face recognition, and 3D modeling. OpenCV supports multiple programming languages, including C++, Python, and Java, making it accessible to a broad range of developers.

Originally developed by Intel, OpenCV has become a common tool in both research and industry applications. Its functionality and performance optimization for real-time image processing have made it useful for projects requiring advanced computer vision capabilities.

Repo: https://github.com/opencv/opencv
GitHub stars: 75K+
Contributors: ~1600
License: Apache License 2.0, 3-clause BSD license

Source: OpenCV

 

5. Apache Spark

Apache Spark is an open source unified analytics engine for large-scale data processing. It provides a framework for distributed computing, enabling rapid data processing and analysis. Spark supports various data sources, including Hadoop Distributed File System (HDFS), S3, and Cassandra, and offers APIs in Java, Scala, Python, and R.

Spark’s capabilities extend to machine learning with its built-in library, MLlib, which supports scalable machine learning algorithms. The real-time data processing capabilities of Spark, combined with its ecosystem and active community, make it a useful tool for big data and AI applications.

In addition, Ocean for Apache Spark is an automated cloud infrastructure and application management system for Spark, offering the power and flexibility of Kubernetes for Spark applications.

Repo: https://github.com/apache/spark
GitHub stars: ~40K
Contributors: ~2100
License: Apache License 2.0

Source: Apache Spark

 

6. Scikit-learn

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN. Built on NumPy, SciPy, and matplotlib, Scikit-learn is accessible for beginners while offering adequate functionality for advanced users.

The library is known for its ease of use, integration with other Python data science tools, and extensive documentation. Scikit-learn’s active user community contributes to its continuous development, ensuring that it remains up-to-date with the latest advancements in machine learning.

Repo: https://github.com/scikit-learn/scikit-learn
GitHub stars: ~60K
Contributors: ~2900
License: New BSD license

Source: Scikit-learn

 

7. Hugging Face Transformers

Hugging Face Transformers is an open-source library that specializes in Natural Language Processing (NLP) and Natural Language Understanding (NLU) tasks. It provides pre-trained models for various applications such as text classification, translation, summarization, and question answering. Built on top of PyTorch and TensorFlow, Hugging Face Transformers makes NLP accessible to all developers.

The library offers an intuitive API and supports thousands of pre-trained models, making it easy to integrate advanced NLP capabilities into applications. The community around Hugging Face Transformers contributes to documentation, tutorials, and continuous improvements. This has positioned the library as a popular resource for NLP research and production.

Repo: https://github.com/huggingface/transformers
GitHub stars: ~130K
Contributors: ~2600
License: Apache License 2.0

Source: Hugging Face

 

8. H2O.ai

H2O.ai provides an open-source AI platform that automates machine learning (a trend known as AutoML). The platform offers tools for building and deploying AI models with minimal intervention, making machine learning more accessible to non-experts. H2O.ai supports distributed computing, enabling the processing of large datasets in parallel.

The platform includes a variety of pre-built algorithms for classification, regression, clustering, and anomaly detection. H2O.ai’s integration capabilities with data science tools and environments increase its utility in diverse AI applications.

Repo: https://github.com/h2oai/h2o-3
GitHub stars: 6K+
Contributors: ~180
License: Apache License 2.0

Source: H2O.ai

 

9. Meta LLaMA 3

Meta LLaMA 3 is an open-source language model developed by Meta, available in 8 billion, 70 billion, and 405 billion parameter versions. It is suitable for tasks such as coding, problem-solving, translation, and dialogue generation, thanks to its nuanced understanding of language and training on a dataset seven times larger than its predecessor, LLaMA 2.

Meta ensures responsible use of LLaMA 3 through comprehensive guidelines and tools. The Responsible Use Guide (RUG) and updated trust and safety tools, including LLaMA Guard 2, Code Shield, and Cybersec Eval 2, help maintain high standards of security and compliance.

Repo: https://github.com/meta-llama/llama3
GitHub stars: ~25K
Contributors: ~25
License: Limited Meta license

 

10. Mistral AI

Mistral AI is a major provider of open-source AI models, aiming to maximize accessibility and efficiency in AI technology. Their flagship models, such as the Mistral 7B and Mixtral series, use advanced architectures to deliver powerful performance across various applications, including natural language processing and code generation.

The Mixtral 8x22B, for example, is known for its efficiency, activating 39 billion parameters out of 141 billion, and outperforming larger models like the Llama 2 70B in benchmarks. These models support multiple languages, offer extensive context windows, and include features like native function calling and JSON mode.

Repo: https://github.com/mistralai/mistral-inference
GitHub stars: 9K+
Contributors: ~20
License: MNPL, Apache License 2.0

Source: Mistral AI

 

Easily integrate AI tools with Instalclustr

With Instaclustr’s suite of open source technologies and managed platforms, organizations can easily and seamlessly integrate numerous AI tools and frameworks into their data pipelines. Instaclustr empowers organizations to leverage the incredible power of AI to enable advanced analytics, predictive modeling and automation, and more.

One key advantage of Instaclustr’s easy integration with the most popular AI tools is the ability to process and analyze large volumes of data in real-time. As AI models often require significant computational resources and benefit from parallel processing capabilities, Instaclustr is designed specifically for scalability and performance. This enables organizations to process massive datasets and train complex AI models efficiently–and allow them to derive insights and make predictions in near real-time.

Instaclustr’s integration with AI tools also facilitates the deployment and operationalization of AI models. Once an AI model is trained, it needs to be deployed and integrated into existing systems to make predictions or automate processes. Instaclustr provides the necessary support for deploying AI models, ensuring high availability, scalability, and security. This enables organizations to seamlessly integrate AI capabilities into their applications, workflows, or data processing pipelines.

For more information:

• OpenSearch® Sink Connector for Apache Kafka® is now Available on the Instaclustr Managed Platform
• Powering AI Workloads with Intelligent Data Infrastructure and Open Source