16th October 2024, Kathmandu
As AI innovation accelerates, organizations are facing challenges related to the high costs of AI development, a reality highlighted by Gartner. This has led many to turn to AI packages—collections of tools designed to streamline AI and machine learning (ML) development—enabling more cost-effective, efficient, and feasible solutions.
Top 10 Most Popular AI Packages
In this article, we explore the top 10 AI packages of 2024 based on research conducted on millions of cloud assets by the Orca Research Pod.
What are AI Packages?
AI packages make it faster, easier, and more affordable to build custom AI or ML applications. These packages consist of pre-developed modules and tools that allow developers to create, train, and deploy AI models without starting from scratch. AI packages accelerate model training and simplify AI development.
Top 10 AI Packages of 2024
According to the Orca 2024 State of AI Security Report, here are the most popular AI packages used in cloud environments:
1. Scikit-learn (43% adoption)
An open-source Python module built on SciPy, NumPy, and matplotlib, scikit-learn enables data scientists to create machine learning models for tasks such as classification, regression, clustering, and more.
2. NLTK (38% adoption)
The Natural Language Toolkit (NLTK) is a platform for building NLP applications in Python. It provides tools for parsing, classification, semantic reasoning, and tokenization, making it popular among linguists and developers.
3. PyTorch (31% adoption)
A machine learning framework developed by Meta AI, PyTorch is widely used for deep learning models and artificial neural networks. Its rapid prototyping capabilities make it a top choice for developers.
4. TensorFlow (26% adoption)
TensorFlow, developed by Google, is an open-source library that supports machine learning and deep learning projects on various platforms, offering extensive applications in areas like NLP and image recognition.
5. Transformers (23% adoption)
Created by Hugging Face, Transformers allows developers to access pre-trained models for a variety of tasks such as text classification, image recognition, and audio processing. It integrates with Jax, PyTorch, and TensorFlow.
6. LangChain (22% adoption)
LangChain is a framework that helps developers build applications using large language models (LLMs). It simplifies the creation of LLM-powered apps without requiring fine-tuning or retraining.
7. CUDA (20% adoption)
Developed by NVIDIA, CUDA enables parallel computing on GPUs, accelerating AI training and inference. It remains popular for its scalability and support in AI development.
8. Keras (19% adoption)
A neural network library built on top of Python, Keras integrates with TensorFlow, PyTorch, and Jax, providing a simple interface for deep learning model development.
9. PyTorch Lightning (18% adoption)
PyTorch Lightning is a framework that simplifies the development of deep learning models by organizing PyTorch code, improving flexibility and reproducibility.
10. Streamlit (11% adoption)
An open-source library that allows data scientists to build dynamic data applications without web development expertise, Streamlit is popular for its ease of use and real-time prototyping.
2024 State of AI Security Report
The data for the top 10 AI packages comes from Orca’s 2024 State of AI Security Report, which also examines AI services, models, and security risks in production environments. The report highlights vulnerabilities in cloud environments such as AWS, Azure, Google Cloud, Oracle Cloud, and Alibaba Cloud.
AI Packages and Orca’s Cloud Security Platform
Orca recently launched its AI Security Posture Management (AI-SPM) solution, which provides risk detection for over 50 AI packages and models, including those listed above. The platform also features AI-driven remediation and search tools, enhanced with GPT-4o integration, supporting plain language queries in over 50 languages.
For more information: Top 10 Most Popular AI Packages 2024
