AI for Academic Success: Tools, Ethics, and Best Practices

Open Source AI - 12 Platforms and Tools to Know
1. Apache SystemDS

Apache SystemDS is an open-source machine learning system for the end-to-end data science lifecycle, from data integration, cleaning and feature engineering to distributed model training, deployment and serving.

• Provides declarative, R-like and Python-like languages along with related APIs.
• Optimizer compiles high-level languages and APIs into hybrid runtime plans, automatically selecting between in-memory operations and distributed execution (e.g., on Apache Spark) based on data and cluster characteristics.
• Supports multiple execution modes, including Spark MLContext, Spark Batch, Standalone and JMLC.
• Released under the Apache License 2.0, with current downloads available for the 3.x version line.

2. ClearML

ClearML is an open source platform designed to automate, monitor and orchestrate machine learning development, from research to production.

• Allows users to integrate any machine learning, deep learning or language model, on any large data set in any architecture with their existing AI framework or stack.
• Comes with optional commercial add-ons such as priority support, managed services, permission management and well-defined SLAs.
• Vendor and cloud agnostic.
• Supports on-premise, air-gapped, cloud and hybrid environments.

3. DeepSeek

Developed by the Chinese AI startup by the same name, DeepSeek is an AI chatbot powered by the company’s open source R1 and V3.1 models.

• Offers an online chatbot option comparable to that offered by ChatGPT, Gemini and Claude.
• Increased efficiency means it operates at a fraction of the cost of competing models with similar accuracy at the time of its release.
• Responses can be accompanied with a “chain of thought” output demonstrating to the user how the model arrived at its conclusions.
• Available under an MIT License, allowing for free use and the right to modify the model and distribute new versions.

4. Hugging Face

Hugging Face is a platform and community that helps users build machine learning models by providing the infrastructure to train, run and deploy AI applications.

• Maintains a library of more than a million open source models, which support tasks ranging from natural language generation to computer vision.
• Has a library of more than 200,000 sets of text, image, video, audio and even geospatial data.
• Developed its own open source language model called BLOOM, which primarily handles cross-lingual content creation and translation tasks.
• Makes it easy for users to share resources, models and research openly, helping to reduce model training time and resources.

5. H2O.ai

H2O.ai is a fully open source, agentic AI platform that offers a range of algorithms and automated tools tailored for tasks like data preprocessing, feature engineering and model selection.

• Has a library of machine learning algorithms, including supervised and unsupervised learning.
• Operates its own generative AI tool to help users analyze documents, summarize content and generate new content.
• Offers a tool that can automatically label users’ data, as well as a tool to extract information from unstructured text data using intelligence character recognition and natural language processing.
• Has built-in intelligence to anticipate schemas of incoming data sets.

6. Keras

Keras is a Python-based neural network library focused on building and training deep learning models.

• Supports convolutional neural networks and recurrent networks, as well as common utility layers like dropout, batch normalization and pooling.
• Runs on top of various frameworks, including TensorFlow, PyTorch and JAX.
• Can be used as a cross-framework language to develop custom components, such as layers, models or metrics.
• Offers dozens of deep learning models, along with pre-trained weights, that can be used for prediction, feature extraction and fine-tuning. 

7. LangChain

LangChain is an open source framework for building applications based on large language models, providing tools to improve their customization, accuracy and relevancy.

• Its use cases largely overlap with those of language models in general, including document analysis and summarization, conversational AI and synthetic data generation.
• Provides APIs with which developers can interface with both open and proprietary models.
• Enables the architecting of RAG systems, offering tools to transform, store, search and retrieve information that refine a model’s responses.
• Allows developers to include memory capabilities in their systems, including simple memory of recent inputs and complex memory to analyze historical messages to return the most relevant results.

8. OpenCV

OpenCV (Open Source Computer Vision Library) is a library of AI algorithms with comprehensive computer vision capabilities for real-world applications.

• Offers thousands of algorithms for tasks like object detection, facial recognition, video analysis and more.
• Primarily designed in C++, along with wrappers in Java and Python.
• Runs on desktop operating systems like Windows, macOS and Linux, as well as mobile operating systems like Android, iOS and Maemo.
• Runs a community forum and offers several free courses.

9. PyTorch

PyTorch is a framework based on the Python programming language and Torch library that is used for training neural networks. It was originally developed by Meta AI and is now part of the Linux Foundation, a non-profit that supports open source software projects.

• Has an extensive ecosystem of tools and models like TorchVision (for computer vision tasks), TorchText (for natural language processing tasks) and TorchAudio (for audio processing tasks).
• Uses Tensors (specialized data structures that run on GPUs) to encode the inputs, outputs and parameters of a model, helping to accelerate the computing process.
• Has its own automatic differentiation engine called “torch.autograd” to power neural network training, enabling it to have some of the fastest training times among machine learning frameworks.
• Supported by all major cloud providers.

10. Scikit-learn

Scikit-learn is an open source Python library designed for machine learning, predictive analytics and statistical modeling.

• Features various classification, regression and clustering algorithms, including support vector machines, random forests and K-means.
• Provides various tools for model fitting, data preprocessing, model selection, model evaluation and more.
• Designed to interoperate with Python numerical and scientific libraries, including Pandas, NumPy and SciPy.
• Has an active community and extensive learning resources.

11. TensorFlow

TensorFlow is an open source software library created by Google that helps developers build and deploy machine learning models on desktop, mobile, web, cloud and IoT devices.

• Offers a selection of pre-trained and research models that users can fine-tune and customize with additional data to perform new tasks.
• Offers several tools to gather, clean and process data at scale, including standard data sets for initial training, data pipelines for loading data, and tools to validate and transform large data sets.
• Supports multiple coding languages, including Python and JavaScript.
• Offers free tutorials, courses and certifications to help people learn the basics of AI development.

12. Together AI

Together AI provides a range of open source research, models and data sets. Its cloud services help developers, researchers and organizations to train, fine-tune and deploy generative AI models faster and cheaper.

• Provides access to more than 200 open-source and specialized models through serverless endpoints, including Llama 3, Stable Diffusion XL and Mixtral 8x22B, allowing users to fine-tune them.
• Builds custom mo dels from scratch, starting from data collection all the way through to evaluating model performance against popular benchmarks.
• Offers high-end compute clusters for training and fine-tuning, which include Nvidia’s H100 and H200 GPUs.
• Allows teams to easily share fine-tuned models, enabling them to collaborate on testing, analyze usage and set up API keys for each stage of the development process.

Source: https://builtin.com/artificial-intelligence/open-source-ai