> ## Documentation Index
> Fetch the complete documentation index at: https://anaconda.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Developing models

Data Science & AI Workbench makes it easy for you to create models that you can [train](./train-model) to make predictions and facilitate machine learning based on deep learning neural networks.

You can [deploy](./deploy-model) your trained model as a REST API, so that it can be [queried and scored](./query-score-model).

The following libraries are available in Workbench to help you develop models:

* **Scikit-learn**: for algorithms and model training.
* **TensorFlow**: to express numerical computations as stateful dataflow graphs.
* **XGBoost**: a gradient boosting framework for C++, Java, Python, R and Julia.
* **Theano**: expresses numerical computations & compiles them to run on CPUs or GPUs.
* **Keras**: contains implementations of commonly used neural network building blocks to make working with image and text data easier.
* **Lasagne**: contains recipes for building and training neural networks in Theano.
* **Neon**: deep learning framework for building models using Python, with Math Kernel Library (MKL) support.
* **MXNet**: framework for training and deploying deep neural networks.
* **Caffe**: deep learning framework with a Python interface geared towards image classification and segmentation.
* **CNTK**: cognitive toolkit for working with massive datasets to facilitate distributed deep learning. Describes neural networks as a series of computational steps via a directed graph.