Learn the Top 6 Artificial Intelligence Frameworks In 2024

Learn the Top 6 Artificial Intelligence Frameworks In 2024

What are Artificial Intelligence frameworks? 

Artificial Intelligence Frameworks are tools, interfaces, libraries, and AI entities that help developers train and design AI models. They are the basic building blocks of machine learning and deep learning models. Not only that, it also serves as the driver of accelerating technology to its territories of innovation.

In the past years, AI was considered fictional. However, artificial intelligence frameworks have made AI a reality in 2024. Artificial intelligence has become more humane, from human robots like Sophia to Google’s Siri. 

Moreover, the demand for machine learning and Artificial intelligence has grown enormously. The AI community has increased exponentially due to the evolution of artificial intelligence frameworks that make AI learning much easier and faster.

Artificial intelligence frameworks are the backbone of AI. These frameworks drive AI research and development to the next level. Let’s explore how AI creates differences in working styles and enhances knowledge and skills in your domain.

Learn how to use Artificial intelligence frameworks correctly with the Be10x AI tools course at Rs.9. 

How do you learn artificial intelligence for beginners?

This article will explore famous artificial intelligence frameworks

that can help you start your AI framework learning journey. 

1. Tensor Flow:

You have heard this before if you are working in the AI sector or if you have researched “how AI works” out of curiosity. It is a robust open-source framework that supports deep learning. The AI platform that can be accessed on mobile devices has access to tensor flow. This is part of the Google family. The tensor flow is an apt tool for statistical program development. It offers various levels of training, modal abstraction training at any level the user prefers. 

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Some of the main features of Tensor Flow are:

  • Provide proper, well-documented manuals for people.
  • Provide easy programming through a multi-programming interface.
  • Major open-source community due to strong growth.


  • Python learners use tensor flow, which is extremely popular nowadays.
  • This AI framework has high computational power. CPU or GPU can be used for it.
  • The machine models are used to create the computational graph.
  • It is famous for its flexibility and scalability and is most suitable for major AI applications.


  • It needs somewhere in pre-trained AI models.
  • This is quite time-consuming. As the input will pass through multiple nodes. 

2. PyTorch: 

Facebook’s AL research lab developed PyTorch, a renowned AI framework. It is also an open-source framework for artificial intelligence and Machine learning. It helps with various tasks, ranging from research prototyping to machine learning. 

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It is a rich ecosystem with tools and libraries that support computer vision development. The community can answer users’ queries on various platforms, such as Slack, Maintainers, contributionGuide, Governance, DesignPhilosophy, and Contributors. This AI framework helps in executing one-to-one communication using Python or C++. 

Companies like Amazon advertising and Salesforce use PyTorch to drive scale and multitask learning. Universities like Standford University also use PyTorch to research new algorithm approaches efficiently.   

Let’s explore its features one by one.


  • It is capable of being developed on the cloud platform. 
  • It is the integration of powerful tools and libraries for Python and C++.
  • It helps optimize tasks and scales up your distributed training practice.


  • You can write your layer.
  • It is flexible, fast and efficient.
  • You can use numerous modular that will combine easily.


  • It cannot support your code for training.
  • It doesn’t have commercial support.

3. Microsoft Cognitive Toolkit:

It is a popular AI framework that Microsoft powers. It is an open-source toolkit for in-depth learning. It is a faster and more versatile framework based on neural networks. It supports text messages and voice modeling. 

CNTK allows users to combine popular models like Feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs) to increase efficiency. CTNK is used in Python and C++, and its model description language is used. You can use the CNTK model evaluation functionality from your Java program. It will work efficiently on 64-bit Linux or 64-bit Windows operating systems. 


  • Provide efficiency, scalability, speed, and high-level integration.
  • Provided you with build elements like hyperparameter tuning, supervised learning models, reinforcement, CNN, RNN, etc.
  • It’s a network that can be expressed effectively like full high-level and low-level APIs.


  • It has multiple servers at once because it supports Python and C++-type languages. 
  • It makes the learning process more quick and faster.
  • It provides distributed training to train machine models.


  • It lacks mobile ARM.
  • It also needs to improve a visualization bond.

4. Caffe:

It is a deep learning framework with preloaded trained neural networks developed by Berkeley AI Research (BAIR) and community contributors. It was first released under the BSD 2-Clause License. Its expressive architecture encourages application and innovation. 

The Caffe model is defined by configuration without hard coding. It can easily switch between the CPU and GPU by training on a GPU machine and deploying to mobile devices. This framework has excellent image processing capabilities and the extended support of MATLAB. 

Features of Caffe: 

  • It supports a few input formats and only one output format. 
  • Major modifications are available in patches. 
  • Multi-GPU training is partially supported.
  •  It provides an active open-source community for one-to-one interaction.
  • The code for models is written in plain text. 
  • It has massive speed and high-efficiency work.


  •  It supports the modeling of convolutional neural networks(CNN).
  • Fast computing numerical tasks are done.


  • It is comparatively fast in handling the visual processing of images but needs to be capable of handling complex data. 

5. Amazon Machine Learning:

Amazon AI community is a high-trending AI community that offers high-end support for developing self-learning tools. Amazon framework is the most comprehensive set of AI and ML services, infrastructure, and implementation resources

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It solves real-world problems in business and helps in innovation. The user base of this framework solves common business problems with AI by using the generation AI application. It adds AI to your business application. 

Features of Amazon Machine Learning:

  • These are tailored tools for different levels of experience in AWS, whether you are a developer, business owner, data scientist, or beginner.
  •  It ensures the security of all the data. It keeps the data encrypted.
  • It helps to provide tools for data analysis. 


  • You don’t need to write a lengthy code with this framework. 
  • Mostly used by data scientists, developers, and ML researchers. 


  •   It needs more flexibility for the developer. 
  • It lacks data visualization.

6. Scikit-Learn: 

It is open-source software that is commercially usable—BSD license. It is a Python library built for machine learning. It is the best and simplest tool for data analysis. It is accessible to everybody. It is built on NumPy, SciPy, and Matplotlib.

Feature of Scitkit Learn:

  • It uses two Python libraries- NumPy and SciPy.
  • It is free from any legal bindings.   
  • It is used for spam detection, image recognition, stock pricing, etc.


  • It is a versatile library that serves real-world solutions.
  • It provides an online worldwide community for the authors, and contributors will keep updating. 
  • It offers API’s for algorithm integration with different platforms.


  • There are better options for in-depth learning. 

Conclusion :

These are some of the most popular artificial intelligence frameworks to help establish a solid AI foundation. Now, you will learn how Generative AI works for real-world applications and how to deploy it in real-world applications. After reading this guide, you will be curious about what you will get from Generative AI. Check out our most featured AI learning workshop.

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