To deploy AI and ML, the machine learning frameworks play a vital role. They are tools, libraries and resources which facilitate AI implementation. Here we make you aware of 10 ML Frameworks.

1. TensorFlow : It has been developed by Google Brain. It is both flexible and scalable. It facilitates the deployment of ML models across the platforms and devices. It is supported by APIs.

2. PyTorch : It has Python interface and facilitates computational graph. It is developed by Facebook’s AI Research Lab. It is useful in building and training deep learning models.

3. Keras : It is based on TensorFlow and is useful for building and training deep learning models. It allows experimentation with different architectures and hyper-parameters. It is preferred both by the novices and experienced professionals.

4. Scikit-learn : It is a library in Python. It has a good collection of algorithms and tools for data preprocessing. It helps feature slection and model evaluation.

5. Microsoft Cognitive Toolkit (CNTK) : CNTK is a deep learning framework developed by Microsoft Research. It supports distributed training across multiple GPUs and machines. It suits large scale projects.

6. Theano : It is a Python library. It facilitates computation and optimization of math expressions used in deep learning. It is used in building and training neural networks.

7. MxNet : It is a deep learning framework supporting multiple programming language (Python, R and Julia). It facilitates building models and deploying them across platforms and devices. It has modular design.

8. Caffe : It is a deep learning framework. It is suited for image classification, object detection and segmentation. It is preferred for CV.

9. Torch : It is scientific computing framework. It is used to build neural networks and image processing.

10. xGBoost : It stands for Extreme Gradient Boosting — optimized implementation of gradient boosting algorithms.