Data Science & AI Workbench makes it easy for you to create models that you can train to make predictions and facilitate machine learning based on deep learning neural networks. You can deploy your trained model as a REST API, so that it can be queried and scored. 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.