Popular Platforms for Artificial Intelligence (AI) Development

There are many platforms that are popular for artificial intelligence (AI) development. These platforms provide tools and libraries for tasks such as data preprocessing, model training, evaluation, and deployment. They can be used to develop a wide range of artificial intelligence applications, including image and speech recognition, natural language processing, and predictive analytics.

Some of the most widely used platforms include:

• TensorFlow: an open-source platform for machine learning and artificial intelligence developed by Google.

• PyTorch: an open-source machine learning platform developed by Facebook’s AI Research lab.

• Cuda (Compute Unified Device Architecture): is a parallel computing platform and programming model created by NVIDIA for general-purpose processing on its own Graphics Processing Units (GPUs). In simpler terms, it allows developers to harness the immense parallel processing power of GPUs to significantly accelerate certain types of computational tasks compared to traditional CPUs.

• Keras: an open-source neural network library written in Python that can run on top of TensorFlow.

• scikit-learn: an open-source machine learning library for Python.

• Azure Machine Learning: a cloud-based platform for developing and deploying machine learning models.

• AWS Machine Learning: a cloud-based platform for developing and deploying machine learning models.

• IBM Watson: a cloud-based platform for developing and deploying artificial intelligence and machine learning models.

• Apple Core ML: a framework for integrating machine learning models into iOS, iPadOS, macOS, watchOS, and tvOS applications.

• Google Cloud AI Platform: a cloud-based platform for developing and deploying machine learning models.

• Amazon SageMaker: a cloud-based platform for developing and deploying machine learning models.

• Deeplearning4j: an open-source deep learning platform for the Java Virtual Machine (JVM).

• H2O: an open-source platform for machine learning and predictive analytics.

• Theano: an open-source platform for developing and evaluating machine learning models.

• MXNet: an open-source deep learning platform that supports multiple programming languages.

• Caffe: an open-source deep learning framework developed by the Berkeley Vision and Learning Center (BVLC).

• Microsoft Cognitive Toolkit (formerly known as CNTK): an open-source deep learning platform developed by Microsoft Research.

• Neural Designer: a commercial platform for developing and deploying machine learning models.

• RapidMiner: a commercial platform for developing and deploying machine learning models.

• KNIME: a commercial platform for developing and deploying machine learning models.

• DataRobot: a commercial platform for developing and deploying machine learning models.