20 Open Source Tools I Recommend to Build, Share, and Run AI Projects

Open source AI tools offer ML developers and data scientists a cost-effective way to build, share, and run AI projects without the limitations of proprietary software. By eliminating high licensing fees, these tools let you reallocate resources to scale projects or experiment with new ideas.

With open source flexibility, you can customize solutions to fit your needs, adapting them to suit any model or deployment approach. Similarly, access to a global community means continuous improvements, troubleshooting, and new features that keep you working with the latest advancements.

Open source tools are also designed for easy integration, allowing you to combine multiple resources for a streamlined workflow. In this post, I'll share 20 open-source AI tools you can use to run your AI/ML projects. Trust me, they’re all worth a look.

Why these 20 open source AI tools?

I carefully selected these open source tools based on three key factors: their active and strong community, user-centered core functionality, and alignment with the various stages of the ML and data science lifecycle.

Let's explore these top picks in detail and why each tool is worth considering for your next project.

To give you a quick glance at the tools, here’s a list of 20 open source tools you can use to power your AI projects:

Model packaging and deployment

KitOps
MLflow
Streamlit
Gradio
Rapid Miner

Data and model versioning

DVC (Data Version Control)

Pipeline and workflow orchestration

Flyte
Metaflow
MLRun
Apache SystemDS
Kedro
Mage AI

Model building and training frameworks

TensorFlow
Hugging Face Transformers
H2O.ai
Detectron2
Cerebras-GPT
Rasa
OpenCV

Data validation and testing

Deepchecks

Let's now explore the open source tools in detail.

Model packaging and deployment

These tools support packaging and deployment and provide reproducibility.

1. KitOps

Working on an ML project requires many hands; more often than not, everyone works in isolation. While this process might ultimately lead to a great product or project, it introduces the issue of managing and keeping track of every component—data, code, configurations, artifacts, metadata, and ML model—used throughout the project’s lifecycle.

KitOps solves this problem by using a Kitfile to locate the components and ModelKit an OCI-compliant packaging format, to package your components into a single, standardized unit. ModelKit uses immutable OCI-standard artifact technologies similar to container-native technologies, making it tamper-proof.

In addition to packaging your components as a single unit, KitOps can version and unpack individual components. This tool also integrates with various tools and platforms, from Docker Hub to Azure ML and Comet ML. To further support this, KitOps provides a detailed list of tools compatible with ModelKit packages. This feature simplifies dependency management, streamlines workflows, and improves collaboration across development teams.

The key advantage of KitOps is that everything can be done via Kit CLI. To learn more about KitOps, check out their beginner-friendly documentation and start by building a Retrieval-Augmented Generation (RAG) pipeline.

2. MLflow

MLflow is an open source platform for managing the machine learning project lifecycle, from model development to deployment and performance evaluation. It is beneficial for several reasons.

MLflow makes reproducing results, deploying, and managing your models easy by tracking your ML experiments. The platform is also environment-agnostic and can be accessed via a REST API and Command Line Interface (CLI).

It has seven components:

MLflow model registry is used to store your ML models. It comes with versioning and annotating capabilities.
MLflow Tracking is used to track experiments and measure model performance.
MLflow Models for deploying your ML projects across cloud platforms.
MLflow projects are used to pack your projects to ensure reusability, reproducibility, and portability.
MLflow Prompt Engineering UI handles your prompt engineering task.
MLflow Deployments for LLMs offers APIs to access SaaS and OSS LLM models.
MLflow Recipes for structuring ML projects for real-world deployment scenarios.

MLflow also integrates with various tools and platforms, from PyTorch and LangChain to HuggingFace, OpenAI, Keras, and TensorFlow. You can start building models and generative AI applications on MLflow by following this tutorial.

3. Streamlit

Streamlit is an open source platform that turns your Python scripts into shareable web apps in minutes with just a few lines of code. These interactive web apps are often the demo apps for most ML projects. The most fascinating thing about this platform is that it requires no front-end or back-end experience. Moreover, Streamlit lets you create these sites without HTML, CSS, or JavaScript, without defining routes and focusing on handling HTTP requests.

Streamlit also allows developers to build and deploy powerful generative AI apps in under a minute. Their tutorial pages cover various topics, from how to build an LLM app using LangChain and how to deploy applications in Streamlit.

4. Gradio

Like Streamlit, Gradio is an open source tool for sharing your ML models as web apps with the public. This tool creates an interactive interface for your model. This customizable interface supports integration with popular frameworks like PyTorch and Scikit-learn.

Besides creating a shareable link, Gradio allows developers to present and embed their model interface in Python notebooks to showcase their model capabilities. This link can be generated via a Pythonic API and supports multiple input types, from text to image, audio, and video.

You can build an ML model and share your first application with this Gradio tutorial.

5. RapidMiner

RapidMiner Studio is a software and data science platform for data analysts and ML engineers. It simplifies your project's data processing, mining, machine learning, and model deployment stages. Though this platform is mainly geared toward developing and deploying predictive analytics applications, it still offers users a wide variety of supervised, unsupervised, and reinforcement learning models.

Some features that make Rapid Miner appealing are its pre-built operators, drag-and-drop functionality, interactive visualization, collaborative environment, and visual programming interface, allowing users to create ML workflows without writing code.

The best way to learn how to use RapidMiner Studio is via the community tutorial s.

Data and model versioning

These tools manage datasets, models, and code versioning for reliable tracking and reproducibility.

6. DVC (Data version control)

DVC can be described as the Git for your data science project. Data Version Control is a version control application that manages and tracks changes to data, ML models, and experiments. This way, you can keep your Git repository lightweight while ensuring your project can be reproduced at any point with the right data version. This is pretty similar to how Git tracks your code changes.

In addition to using Git for data versioning, DVC has local experiment tracking for ML experiment management and data pipeline versioning that helps describe how your data are built with other data and codes. The great thing about experiment tracking with DVC is that you don't have to leave Git to evaluate and visualize the metrics and parameters of your ML projects.

Start your DVC experience with a hands-on image classifier project, featuring both data and model versioning.

Pipeline and workflow orchestration

These tools automate complex workflows by optimizing your ML and data processes for scalable production.

7. Flyte

Flyte is an open source workflow orchestration platform that allows developers to build, transform, and deploy data and ML workflow through their Python SDK. This way, you can build and execute scalable, maintainable, reproducible pipelines for data processing and machine learning.

To learn more about Flyte, check out the Flyte Blog.

8. Metaflow

Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform.

You can get started with Netflix’s Metaflow by exploring their tutorials.

9. MLRun

MLRun offers developers an AI orchestration framework for managing ML and generative AI (GenAI) applications by automating data processing, model development, tuning, validation, and optimization of ML models. This platform also allows users to deploy scalable real-time data pipelines across on-prem, hybrid, and cloud environments, thereby streamlining the entire ML project lifecycle for developers.

A great place to start with MLRun is its tutorials and examples page, which demonstrates how to use MLRun to implement and automate ML workflow across the different stages.

10. Apache SystemDS

Apache SystemDS (Formerly Apache SystemML) is an end-to-end data science and ML system that manages your data science projects, from integration and data cleaning to model training and deployment. This platform, built by the Apache software foundation, aims to solve a simple issue.

Typically, models are developed and run on personal computers. While this is great for small datasets, it is unsuitable for large ones because they need more than one node and a more extensive distributed system.

SystemDS address this by offering a large-scale, declarative ML platform with distributed jobs capabilities on Apache Hadoop and Apache Spark. This declarative language is pretty similar to R and Python syntax. It can be compiled into hybrid execution plans of local and in-memory (CPU and GPU) operations.

You can explore Apache SystemDS by trying out some matrix operations from their quickstart guide to learn mor e.

11. Kedro

Kedro is an ML development framework that brings data science projects from pilot development to production by creating reproducible, maintainable, and modular data science code. Kedro has a data catalog for data handling, support pipeline building, and a standardized template for code maintainability and consistency to effectively do this. Its data catalog uses lightweight data connectors to manage and track datasets. This allows you to use the same pipeline to build multiple production-level codes across your system.

Besides versioning your dataset and automating your pipelines, Kedro comes with Kedro-Viz to help you visualize your data, ML workflows, experiments, and their connections. By connections, we mean the relationship between your data and your ML workflow—like how the pipelines handle the tasks and the models and parameters relationships. This tool also integrates with various data science and ML tools. You can start by exploring Kedro’s s paceflights project.

12. Mage AI

Mage AI is an open source data pipeline tool for data transformation and integration. It allows data scientists and machine learning engineers to build, manage, and automate production-ready data pipelines using an interactive, drag-and-drop interface. The platform also leverages AI in the data pipeline building.

A great way to learn about M age AI is to follow this tutorial, which builds an ETL data pipeline that loads and transforms restaurant data into a DuckDB database.

Model building and training frameworks

These tools come with libraries for building and training ML models.

13. TensorFlow

TensorFlow is an end-to-end ML platform for creating, running, training, and deploying ML models to production. Its focus is on deep neural networks.

Note that TensorFlow is an ecosystem on its own. The platform comes with various libraries, ML tools, APIs, and data to help you simplify the process of building and deploying machine learning models. Among these libraries and tools are:

TensorFlow JS for building web ML applications in Javascript.
TensorFlow Lite for deploying ML applications on mobile devices and edge devices.
TensorFlow data for building input production ML pipelines.
TensorFlow Core for APIs like Keras.
Datasets and pre-trained models that are ready for use.
Various tools like the What-If tool and TensorBoard support and accelerate your ML workflows.
Various libraries and extensions from Dopamine to TensorFlow Decision Forests and TensorFlow Lite Model Maker.

A great place to start with the TensorFlow tutorial, which is available to the public.

14. HuggingFace transformers

Besides being one of the biggest open-source providers of Natural Language Processing (NLP) technologies, HuggingFace is an AI and ML platform that democratizes NLP, computer vision, and deep learning, giving data scientists and ML engineers access to pre-trained models. Its pre-trained models are language models trained on big data that leverage various transformer architectures, from the GPTs to BERT.

The HuggingFace also has:

Datasets library repository for accessing and sharing datasets with the community.
Tokenizers for tokenization (converting text into smaller units) for NLP.
Accelerate library for optimizing the training and deploying of your models.
A collaborative environment and other tools within the platform.

To start with HuggingFace, check out their ML tasks page, which hosts various community tutorials, demos, and use cases.

15. H2O.ai

Unlike a few other tools on the list, H2O.ai is more focused on creating and deploying AI models. This platform’s toolkits include various tools and algorithms to help achieve democratized AI. H2O.ai also helps users build Generative AI (Gen AI) applications.

Among the toolkits are:

H2O/H2O-3 is a distributed in-memory ML platform with AutoML for undergoing ML tasks.
H2O Wave is a Python framework for designing and deploying applications with interactive user interfaces.
H2O Driverless AI is an Auto ML tool with auto feature engineering, data visualizations and post-training diagnostics or evaluation models.
Sparkling Water for implementing Spark implementation and advanced machine learning algorithms.
H2O AI Cloud is their fully managed cloud infrastructure.
H2O AutoDoc for automatically creating detailed documentation of your ML models.
H2O MLOps to manage your ML models.
H2O Enterprise Puddle for creating cloud instances.

H2O.ai can also be used in various environments and languages, including Java, Python, Scala, and R. You can elevate your knowledge about H2O.ai by taking their AI University courses.

16. Detectron2

Detectron2 is an ML platform built by Facebook AI Research (FAIR) to enable developers to build ML models for object detection, segmentation, and other visual recognition tasks. The platform has various novel computer vision and object detection algorithms, such as Fast R-CNN, DensePose, Panoptic FPN, and TensorMask. This makes it straightforward for ML engineers to implement various algorithms within this space.

Dectectron2’s documentation has comprehensive but beginner-friendly tutorials to help you get started.

17. Cerebras-GPT

Cerebras-GPT is a platform that hosts about 7 GPT-3 compute-optimal language models, which scale from 111M to 13B parameters. These cutting-edge technology models are trained using the DeepMind's Chinchilla formula, which influences OpenAI's GPT-4 and Google's PaLM 2. Since it is trained with that formula, their models use high-quality datasets, and training computes with the right model size, allowing for more complex tasks and improved performance. Thus, they are most optimized for research labs.

Check out the Cerebras Model Zoo repository to look at their models.

18. Rasa

Rasa is a tool that offers developer ML frameworks for building conversational text and voice-based AI virtual assistants and chatbots with Python and NLP. While the platform comes with pre-trained models, developers can also develop and train their models and add custom actions before deploying them on external platforms for the public.

At the core of Rasa:

Rasa Natural Language Understanding (NLU) is a processing tool for intent classification and entity extraction. It converts text to vectors to identify and understand consumers' input and extract information.
Rasa Core is a chatbot framework that manages ML-based dialogue. It uses a probabilistic model, such as an LSTM neural network.

To see what is possible for Rasa, check out their community showcase and take their ML courses to get started.

19. OpenCV

This platform is best known for its library of real-time computer vision programming functions.

OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision.

You can build your first OpenCV project by exploring the community OpenCV Tutorials.

Data and Model Versioning

These tools validate data and test your models to ensure accuracy and reliability in production.

20. Deepchecks

Deepchecks focus more on data validation and machine learning testing. With Deepchecks, you can build fast without compromising model performance and data integrity. This package does this by comprehensively and continuously validating your model as you build to identify issues. The best part is that you can automate all this and save your results as artifacts with GitHub Actions.

Learn more about Deepchecks by exploring their Interactive checks demo.

Wrapping up

As you probably know, open source is about access, impact, and making technology accessible to the general population. Open source AI tools take this further by allowing everyone to contribute to how AI can solve issues at scale. So, while the impact of AI so far has already been remarkable, it's only just getting started.

Each tool picked out serves a distinct purpose within the ML workflow:

You have tools for packaging and deploying your models.
When it comes to tracking and versioning, your data and model versioning tool comes in handy.
For automating complex workflow, your pipeline and workflow orchestration tool are great!
Model-building and training frameworks serve as the backbone of your project, providing resources for training across various ML tasks.
There are also your data validation and testing tools that help ensure accuracy.

KitOps excels in offering an efficient solution for packaging AI/ML models, datasets, code, and configuration into reproducible artifacts, or ModelKits, that ensure compatibility, reproducibility, and collaboration across teams.

Try KitOps today to optimize your ML workflows. I'm excited to see what you build, share, and run with these open source AI tools. Don’t forget to tag us on social media when you do!

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