Choosing a Framework for your AI Project

Artificial intelligence (AI) is one of the most significant technologies of recent times. From solutions that recognize symptoms of a heart attack to face aging apps and voice assistants. AI has penetrated every field: medicine, social media, entertainment.


A lot of companies, large and small, turn to AI in the hope of expanding the possibilities of their business. The decision to implement AI solutions comes with the need for a relevant framework. In this article, we are going to review 7 popular frameworks for AI.


TensorFlow is used for voice and image recognition as well as for language processing like Google Translate. Works best for complex projects, including multi-layer neural networks. DeepMind, Uber, Airbnb and Dropbox use this platform.

Tensorflow has API in Python on top of C and C++. The framework has a flexible ecosystem of tools and libraries as well as a vibrant community. Tensorflow enables data scientists to use the most recent Machine learning tools. Developers can create and deploy machine learning applications.

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PyTorch was developed by Facebook and used by Twitter and Salesforce among others. It’s a good solution for when you need to teach models fast and efficiently.

PyTorch is an environment for machine learning written in Python. It enables Tensor calculations with GPU acceleration.

With PyTorch, data engineers can change the network behaviour on the go. The framework offers dynamic calculations graphs that allow you to process input and output of a variable length, which is useful if you are dealing with recurrent neural networks. Developers can use C and C ++ with the help of API extension for Python.


Keras is a minimalistic deep learning library in Python that runs on top of TensorFlow, Theano, or CNTK. It is compact, modular and expandable solution that is aimed at accelerating work with neural networks. Keras is chosen for smaller projects due to mediocre performance as compared to Tensorflow.

Keras supports a wide range of neural network layers: convolutional, recurrent, and dense.

The library contains numerous implementations of widely used building blocks of neural networks: layers, target, and transfer functions, optimizers, as well as a variety of tools that simplify work with images and text.

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A deep learning framework in C++ with Python interface. Caffe is characterized by expressiveness, speed and modular structure. Caffe is a good solution for training models, processing images and improving neural networks. It also supports CNN and feedforward neural networks. A few years ago, Facebook released Caffe 2, which is suitable for both mobile and large-scale development in the production environment.


Deeplearning4j ( DL4J) is a good choice for Java programmers. It is a commercial-grade, open-source, distributed library written for Java and Scala. DL4J provides good support for various types of neural networks, from convolutional to recurrent and recursive. This framework is used for image recognition, natural language processing, vulnerability search and text analysis.


XGBoost is an open-source framework that offers a gradient boosting system for C ++, Java, Python, R, Julia. It boasts high performance, flexibility and portability. XGBoost belongs to the realm of classical machine learning frameworks.

Originally, it was a research project of Tianji Chen and Carlos Gestrin as part of the Distributed [Deep] Machine Learning Community, but later it was expanded and presented to the public at the SIGKDD conference in 2016.


One more Python deep learning framework supported by Intel, IBM, NVIDIA, and AWS. It’s a flexible, intuitive solution that runs on multiple GPUs with little effort, supports CUDA and various networks such as feed-forward convnets, recurrent and recursive. This framework is mainly used for voice recognition, machine translation, and key analysis.

AI and machine learning development with NCube

We at NCube build teams around AI stack consisting of Ukraine’s best machine learning, data science, and data engineering talent. The expertise of our engineers spans cognitive computing, computer vision, machine learning, and deep learning. We can build your data science team in the same way we did for Fetch AI.