Top 8 AI/ML Tools: Exploring the Most Popular Machine Learning and Artificial Intelligence Software in 2024

Tech Trends
Apr 15th - 2024

The landscape of business operations and daily life has been significantly reshaped by the emergence of Artificial Intelligence (AI) and Machine Learning (ML). As these fields continue to expand, the assortment of software libraries and development tools enabling AI and ML development also grows.

For those intrigued by the idea of crafting a GPT model to rival ChatGPT or challenge OpenAI’s dominance, this article will highlight some of the top AI and ML software libraries and tools of 2024, including the ones utilized by OpenAI.

While this article will showcase how these tools and libraries are commonly employed, it’s important to note that their applications are not limited to the examples provided. Many of these tools are versatile enough to be applied across various scenarios, though some are particularly well-suited for certain tasks.

1. TensorFlow

Introduced by the Google Brain team in 2015, TensorFlow is an open-source software library excelling in dataflow and differentiable programming. It’s widely used for deep learning and ML applications, such as neural networks.

TensorFlow stands out as a flexible and robust platform for developing and deploying ML models, supporting a range of hardware, making it suitable for both academic research and industrial applications. Its automated differentiation capability simplifies the process of calculating gradients for complex mathematical expressions, streamlining model performance improvement. TensorFlow’s pre-built model library and user-friendly nature allow developers to quickly start working with ML.

Use cases:

  • Image classification: TensorFlow powers object detection and classification for Waymo’s self-driving car project. More information and tutorials are available on TensorFlow’s website and Google Cloud.
  • Natural language processing: TensorFlow offers tutorials for various NLP tasks, including text classification and sentiment analysis.
  • Speech recognition: The TensorFlow Speech Recognition Challenge provides resources for building speech recognition models.
  • AlphaGo: Google DeepMind’s AlphaGo, which defeated the Go world champion, was trained using TensorFlow. More insights can be found on DeepMind’s website and related articles.

Companies like Airbnb, Airbus, ARM, and Intel use TensorFlow, making it the most popular AI framework.

2. PyTorch

PyTorch, an open-source ML library based on the Torch framework and written in Python, was developed by Facebook’s AI research team in 2016. It’s employed in computer vision, NLP, and generative models, known for its user-friendly and flexible nature, ideal for developing and training ML models.

PyTorch’s dynamic computation graph and model-centric high-level API simplify the entry into ML for programmers.

Use Cases:

  • OpenAI: OpenAI has utilized PyTorch to create advanced AI models and has contributed to its development.
  • Computer vision: PyTorch tutorials cover tasks like image classification and object recognition.
  • Natural language processing: PyTorch provides tutorials for various NLP tasks.
  • Generative models: Tutorials for building GANs for image generation are available on PyTorch’s website.

Organizations like OpenAI, Facebook, Nvidia, Tesla, and Uber use the PyTorch framework.

3. Keras

Keras is a free, open-source neural network library with a Python API, designed to enable quick experimentation with deep learning. Its intuitive interface, modularity, and scalability make it perfect for rapid prototyping.

Keras’s high-level API and modular design simplify the creation of deep learning models, making it accessible to a broader range of developers.

Use cases:

  • Image classification: Keras is used for tasks like object detection, with guides available on the Keras blog.
  • Natural language processing: Keras supports NLP tasks with detailed guides for text classification models on its blog.
  • Healthcare: Keras is applied in medical diagnostics, such as disease diagnosis from medical images, with resources available for creating models like pneumonia classification.

Keras is the most popular deep-learning framework on Kaggle.

4. Scikit-learn

Scikit-learn is a Python ML library that provides simple and efficient tools for data analysis and mining. It was developed to make ML tools more accessible to a broader audience and support tasks like classification, regression, clustering, and dimensionality reduction.

Scikit-learn is known for its ease of use and comprehensive nature, offering a variety of pre-built models and algorithms for common tasks, as well as data preprocessing and feature extraction tools.

Use cases:

  • Customer segmentation: Scikit-learn is used to group customers based on behavior or characteristics, and tutorials are available.
  • Fraud detection: It’s also applied to identify fraudulent transactions in financial data.
  • Recommender systems: Scikit-learn is used to suggest items to users based on preferences, with tutorials available.

Companies like Spotify, J.P. Morgan, Evernote, and have adopted scikit-learn, with backing from Microsoft and BNP Paribas Cardif.

5. XGBoost

XGBoost, which stands for “eXtreme Gradient Boosting,” is a toolkit for distributed gradient boosting known for its speed, flexibility, and portability. Launched in 2014 by Tianqi Chen, it’s commonly used for tree-based model building in Kaggle competitions.

XGBoost’s main appeal is its speed, optimized for handling large datasets, and it offers a robust set of tools for model tuning and optimization.

Use cases:

  • Sales forecasting: XGBoost is used to predict future sales.
  • Customer churn prediction: It helps identify customers likely to discontinue services.
  • Stock price prediction: XGBoost is applied in forecasting stock prices, with numerous resources and code examples available online.

XGBoost is supported by companies like Nvidia and Intel.

6. LightGBM

Developed by Microsoft and released in 2016, LightGBM is a gradient boosting framework that focuses on efficiency and scalability, capable of handling large amounts of data.

LightGBM’s main advantage is its speed, making it suitable for large datasets, and it offers a wide array of tools for model refinement and optimization.

Use cases:

  • Credit risk prediction: LightGBM is used to predict loan defaults and research papers and articles are available.
  • Fraud detection: It’s applied in identifying fraudulent transactions in financial data.
  • Sales forecasting: LightGBM is used to predict future sales, and detailed articles are available.

7. FastAI

FastAI, built on the PyTorch framework, was created to simplify deep learning for more programmers. It supports vision, text, tabular, and collaborative filtering models through high-level APIs.

FastAI’s simplicity and user-friendliness, along with its comprehensive set of pre-built models and tools, make it a complete deep-learning solution.

Use cases:

  • Computer vision: FastAI is used for tasks like image classification and object detection, with tutorials on its website.
  • Natural language processing: It supports NLP tasks, with text classification and sentiment analysis tutorials available.
  • Tabular data: FastAI is applied in regression and binary classification for tabular data, with tutorials available.


MATLAB, developed by MathWorks and first released in 1984, is a platform and programming language for numerical computation. It offers ML tools for classification, regression, clustering, and deep learning, and is widely used in academia and research due to its accessibility.

MATLAB’s high-level API and comprehensive suite of algorithms, data pre-processing, and visualization tools make it a complete ML solution.

Use cases:

  • Signal processing: MATLAB is used for tasks like filtering and transforming signals, with dedicated products available on MathWorks’ website.
  • Control systems: It’s applied in designing and simulating control systems.
  • Image processing: MATLAB is used for processing and analyzing images.

Tips for AI/ML Software Usage

When working with AI/ML tools, consider the complexity of algorithms, data quality, computational resources, potential overfitting, and bias in training data. These factors can impact the success and reliability of AI/ML systems.


This selection represents just a fraction of the AI and ML software libraries and tools available. Each tool has its strengths and weaknesses, and the right choice depends on the specific needs of the task at hand. As AI and ML continue to advance, developers must stay informed about the latest developments in the field

Frequently Asked Questions about AI & ML Development

  • What is artificial intelligence?
    AI refers to the capability of machines to perform tasks that typically require human intelligence, such as voice recognition, decision-making, and problem-solving. AI can be categorized as narrow (or weak), designed for specific tasks, or general (or strong), capable of any cognitive task a human can perform.
  • What is machine learning, and how does it work?
    Machine learning is a subset of AI where computers learn from data without explicit programming. It involves data preparation, model training, and model evaluation, with various algorithms like supervised, unsupervised, and reinforcement learning.
  • What is the difference between AI and machine learning?
    AI encompasses a broad range of techniques that enable machines to mimic human intelligence, while machine learning is a specific approach within AI focused on learning from data.

Advances in AI and ML involve identifying problems, preparing data, selecting and implementing algorithms, training models, evaluating performance, and deploying models into production. Continuous monitoring and updating are essential to maintain model accuracy over time. Developers have access to various tools and platforms to facilitate AI and ML development, requiring skills in programming, data analysis, and domain expertise.

Tech Trends
Apr 15th - 2024