Best Frameworks to Use for AI App Development in 2022
Want to build an application with AI and machine learning features? Use any of these AI app development frameworks.
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Artificial intelligence has spread to almost every industry and sector. It has a lot of applications and has the potential to build high-quality inclusive solutions aimed at improving how we live, communicate, interact, enjoy, work, and engage.
As you build AI-enabled applications, it requires a deep understanding of the technology and its implementations. AI app development has become a mainstay of modern digital solutions creation integrated with the latest features and functions to help the users get an immersive experience.
AI development services are easier said than done as it involves a deep-set understanding of development methodologies. As a result, the developers employ frameworks catering to AI app development.
Frameworks help speed up the development process and help create the required solutions at cost-effective prices. Let’s find out the best frameworks for AI and machine learning development tasks you can employ to stay ahead with the technological trends in 2022.
Top 7 AI Frameworks for AI App Development
Why Choose TensorFlow?
- Made by Google
- Suited for Complex Apps
- Support for Advanced Concepts
- Strong Community
TensorFlow Pros and Cons
2. Sci-Kit Learn
Sci-Kit Learn is a specialist framework for artificial intelligence and machine learning development. It is also used in the form of a Python library. Majorly, Sci-Kit is used to build application models with expertise in statistical modeling like classification, regression, and clustering. Going beyond, Sci-Kit also supports dimensionality reduction, preprocessing, and model learning.
With this framework, you can complete both supervised and unsupervised machine learning development while integrating artificial intelligence capabilities.
Why Choose Sci-Kit?
- Data Analysis
- Great for Data Scientists
- Expansion of NumPy and SciPy
Pros and Cons of Sci-Kit Learn
3. Microsoft CNTK
Another open-source toolkit for AI app development, CNTK, is built by Microsoft. The word in the town is that Microsoft CNTK is the response to Google’s TensorFlow. The best part about this framework is that it supports computational heavy datasets and algorithms to build robust solutions for different industries.
Compared to other frameworks on the list, Microsoft CNTK is faster and versatile. It’s compatibility with neural networks helps developers include features that provide voice, text, and message remodeling.
Why Choose Microsoft CNTK?
- Supports Different Languages
- Built-In Componentss
Pros and Cons of Microsoft CNTK
That’s a great name for a framework to build AI-based applications, isn’t it? Sometimes coffee and Caffe are the only two things developers need to provide AI development services having pre-loaded neural networks. That’s not it; these neural networks are pre-trained to build smart and efficient solutions.
Besides typical neural networks, it also supports Convolutional Neural Networks (CNN) and has impressive image processing capabilities. Working with Caffe may feel like it lacks the required elements for complex data handling. But what it lacks in data is compensated in terms of speed and processing.
Why Choose Caffe?
- Strong Architecture
- Better Performance
- Community Support
Pros and Cons of Caffe
Theano is a Python-based AI app development framework and is considered a strong contender of Google’s TensorFlow. Among its other features and capabilities, Theano’s ability to handle multi-dimensional arrays and numeral operations with efficiency stands out.
Such operations (multi-dimensional arrays) require high power capacity, and Theano is one of the best platforms for this. Moreover, Theano’s support for Python also makes it faster in terms of processing and responsiveness.
Why Choose Theano?
- Data-Intensive Computation
- Computer Algebra System (CAS)
Pros and Cons of Theano
Another one of the AI app development frameworks popularly used in the industry, Torch, is known for its ability to complete numerical and scientific operations. Using Torch, you can build clean coded algorithms that can perform the intended functions with speed, reliability, and accuracy.
Why Choose Torch?
- N-Dimensional Array
- Support and Integration
Pros and Cons of Torch
Keras is a unique app development technology meant for AI and machine learning solutions. It's unique because we cannot clearly identify it as a framework. Rather, Keras is an API meant to be used by humans and less by machines.
Why Choose Keras?
- Abstraction Level
- Add New Modules
Pros and Cons of Keras
Artificial Intelligence, Machine Learning, Neural Networks, and other similar innovations are changing the course of our future and the way we build new products and services. While the development languages are primary to this development, these frameworks have made integrating AI and other technologies into an application easier. Hence, to target high-quality creation, we have shared some of the best AI app development frameworks developers can use for their solutions.
Published at DZone with permission of Mahipalsinh Rana. See the original article here.
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