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Project Introduction

Functional Description

Non-Functional Requirements

User Interface

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Milestones

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Problem

There are 2 phases to applying Deep Learning to a ML problem, the first phase is where a neural network is created and trained using training data to generate pre-trained model and In the second phase, this pre-trained model is put to work by running inference(forward-pass) on new data in the customer’s application in production. Model Creation and Training is typically performed by Data Scientists who prefer using Python as a primary language which provides rich set of libraries(numpy, pandas, pillow) etc., to setup the training pipeline. MXNet already has very good support for Python to quickly prototype and develop models.

Inference on the other hand is run and managed by Software Engineers in a production eco-system which is built with tools and frameworks that use Java/Scala as a primary Language.

Inference on a trained model has two different use-cases:

1) Real time or Online Inference - tasks that require immediate feedback, such as fraud detection

2) Batch or Offline Inference - tasks that don't require immediate feedback, these are use-cases where you have massive amounts of data and want to run Inference or pre-compute inference results 

Batch Inference is performed on big data platforms such as Spark using Scala or Java.

Real time Inference is typically deployed on popular web frameworks such as Tomcat, Netty, Jetty, etc. which uses Java. 

With this project, we want to build Java APIs that are easy to use for Inference and lowers the entry barrier is consuming MXNet for production use-cases.

Goals

Proposed Approach

Alternative Approaches considered

Technical Challenges

Open Questions

Milestones

Glossary