Highlights

• Support for building and productionizing end-to-end deep learning applications for big data
• E2E analytics + deep learning pipelines (natively in Spark DataFrames and ML Pipelines) using nnframes
• Flexible model definition using autograd, Keras & transfer learning APIs
• Data preprocessing using built-in feature engineering operations
• Out-of-the-box solutions for a variety of problem types using built-in deep learning models and reference use cases
• Serving models using POJO model serving APIs for web services and other big data frameworks (e.g., Storm or Kafka)