7 AI Frameworks That Make AI Apps Development Seamless
AI has taken the front seat in the app development world in these times of advancement. To make the most out of this opportunity, it becomes imperative to understand the best Artificial Intelligence tools and frameworks currently in the market. In this blog, we are going to highlight the same for you!
Artificial Intelligence is synonymous with digital transformation, and the impact of both of them is very evident in this modern world. AI has been in the developing world for a very long time, but until now, many developers have yet to study it.
But now, According to a survey, the global AI market is expected to reach $190.61 billion by 2025.
So, what’s the reason behind this sudden increase in popularity? Well, there can be many; the power of automation and better customer delivery is the best of them!
Being an app development company, we have sorted the best AI tools below. Keeping in mind that every development team has unique requirements, you can choose from the best Artificial Intelligence tools that are best for you;
Microsoft CNTK
To stay at par with Google, Microsoft introduced CNTK to respond to SensorTower.
Microsoft Cognitive Toolbox, sometimes known as CNTK, is an open-source deep learning toolkit. It describes neural networks as a sequence of computational processes using a directed graph.
This library improves the upkeep of separate computation networks by supplying machine learning methods and model descriptions. It can also construct, train, and execute a variety of deep neural networks.
It features its model description language, BrainScript, and may function as a stand-alone machine-learning tool.
Furthermore, CNTK may be incorporated with Python, C++, or any.NET language, including C# or Java (model evaluation functionality).
Pros:
*Flexible programming language for distributed training
Cons:
*Lacks visualization owing to the network description language
Theano
Since 2007, Theano has enabled extensive scientific research using massive quantities and calculations. It can integrate computer algebra systems with compiler features.
Theano can minimize compilation/analysis overhead when numerous evaluations are executed simultaneously. This also offers symbolic features such as automatic differentiation.
Pros:
*Utilizes GPU to perform data-intensive transactions at a faster speed.
Cons:
*It needs to be integrated with other libraries to attain higher levels of abstraction.
TensorFlow
TensorFlow is a popular machine learning framework or library; the most significant part is that it is a free source. Backed by Google and having a vast client base that consists of Airbnb, eBay, and Coca-Cola, Tensor flow is one of the best examples of AI-based application frameworks.
It is ideal for high-volume sophisticated numerical computations employing data-flow graphs and machine learning for your artificial intelligence applications.
The design of this framework permits computation on any CPU or GPU. It works on a PC, server, or even a mobile device.
Pros:
*Simplified and efficient development to keep the code lean.
Cons:
*Very monotonous and slow as it lacks pre-trained ML models.
Sci-Kit Learn
Sci-kit learns there is a Python library for machine learning.
Scikit-learn is a commercially viable Artificial Intelligence framework and one of the Artificial Intelligence open-source technologies. It is a Python package that allows you to do supervised and unsupervised machine learning.
Using the detailed user guide provided by sci-kit learn, data scientists may readily access resources ranging from multiclass and multilabel techniques to covariance estimation.
Sci-kit learning includes features such as cross-validation and supervised and unsupervised learning algorithms, which aid in developing models.
Pros:
*Several algorithms are available.
Cons:
*No GPU support is available.
Torch
Torch, a Tensor Library comparable to NumPy, professes to prioritize GPUs. It is wrapped in LuaJIT and contains an underlying C/CUDA implementation.
It has increased efficiency and improved deep learning research by giving many methods. Torch users benefit from simple-to-use libraries that allow for the flexible development of complicated neural network topologies.
It aids in operations like slicing and indexing when using a powerful N-dimensional array. It also includes linear algebra and neural network models.
Pros:
*Flexible with several pre-trained models.
Cons:
*Unclear and complex documentation.
Keras
Keras is a popular AI framework with a neural network library written in Python, similar to TensorFlow and CNTK. However, it is not intended to be an end-to-end machine learning framework. Keras is an API that is only designed for people, not machines.
New modules are simple to add, and neural layers, linear functions, and other independent modules can be integrated with little constraints.
This Python deep learning toolkit is a high-level Artificial Intelligence based API that allows modularity and full expressiveness. This may be run on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano.
Pros:
*Hassle-free operation on both CPU and GPU.
Cons:
*It can’t be utilized independently.
AutoML
Created by Google, AutoML was formally co-opted in May 2017 for Google’s neural network architecture search. This is one of the most influential and adaptable artificial intelligence frameworks. The creators of Auto ML are aided in evaluating, enhancing, training, and deploying models based on their data through an intuitive graphical user interface.
Pros:
*Quicker Results due to ML implementation.
Cons:
*Inflexible and ever-changing requirements.
Wrapping Up
AI is here for good and will undoubtedly transform the app development world. Many big sharks of the industries are adopting this technology to improve their business operations and serve better user experience to win battles.
These were the top 7 Artificial Intelligence-based frameworks for us, but we understand that your choices and preferences may differ from ours. Every business has unique needs, and choosing a relevant & efficient tool is the best action.
You are at the right place if you need assistance integrating AI into your business operations. Talk to our experts, and find the right solution for your business!
