Deep Learning Research to Production – An hands on approach using Apache MXNet
Deep Learning Research to Production – An hands on approach using Apache MXNet


Deep Learning (DL) has become ubiquitous in every day software applications and services. A solid understanding of DL foundational principles is necessary for researchers and modern-day engineers alike to successfully adapt the state of the art research in DL to business applications.

Researchers require a DL framework to quickly prototype and transform their ideas into models and Engineers need a framework that allows them to efficiently deploy these models to production without losing performance. We will show how to use Gluon APIs in Apache MXNet to quickly prototype models and also deploy them without losing performance in production using MXNet Model Server (MMS).

In this workshop, you will learn applying Convolutional Neural Network (CNN), a class of DL techniques, in Computer Vision (CV) and applying Recurrent Neural Network (RNN) DL techniques for solving Natural Language Processing (NLP) tasks using Apache MXNet - the two fields in which Deep Learning has achieved state of the Art results.

To learn applying DL in CV problems, we will get hands-on by building a Facial Emotion Recognition (FER) model using advances of deep learning in CV. We will also build a sentiment analysis model to understand the application of DL in Natural Language Processing (NLP). As we build the model, we will learn common practical limitations, pitfalls, best practices and tips and tricks used by practitioners. Finally, we will conclude the workshop by showing how to deploy using MMS for online/real-time inference and using Apache Spark + MXNet for offline batch inference on large datasets.

We will provide Juptyer notebooks to get hands on and solidify the concepts.


Naveen is a Senior Software Engineer and a member of Amazon AI at AWS and works on Apache MXNet. He began his career building large scale distributed systems and has spent the last 10+ years designing and developing it. He has delivered various Tech Talks at AMLC, Spark Summit, ApacheCon and loves to share knowledge. His current focus is to make Deep Learning easily accessible to Software Developers without the need for a steep learning curve. In his spare time, he loves to read books, spend time with his family and watch his little girl grow

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