Real-time recommendation using attention networks with Analytics Zoo on Apache Spark
Who is this presentation for?
- Machine and deep learning practitioners and big data professionals
Attention networks have proven to be very effective when dealing with natural language processing (NLP) tasks such as machine translation and natural language generation. It can also be very effective when dealing with recommendation tasks, especially when the recommendation result is required to generate in sequence in real time at the session level. Traditional approaches like collaborative filtering and content-based recommendation approach struggle to consider the compatibility of recommended results and the current session’s evolution. The attention network is an extremely expressive and high-performance model that learns highly complex relationships from a sequence of data.
Lu Wang and Jennie Wang explain how to build a real-time menu recommendation system to leverage attention networks using Spark, Analytics Zoo, and MXNet in the cloud. Burger King built a highly effective menu recommendation model using the attention network using distributed MXNet, Apache Spark, and Analytics Zoo. You’ll see how to deploy the model and serve the real-time recommendation using both cloud and on-device infrastructure on the company’s production environment.
Analytics Zoo provides unified analytics and an AI platform that seamlessly unites Spark, TensorFlow, Keras, MXNet, PyTorch, and BigDL programs into an integrated pipeline; the entire pipeline can then transparently scale out to a large Hadoop or Spark cluster for distributed training or inference. Analytics Zoo enables easy data analytics solution construction from initial prototyping for algorithm designs on the laptop, experimentation training, and inference on Spark clusters to seamlessly deployment on production with distributed big data pipelines.
- A basic understanding of machine learning, deep learning, and Apache Spark
What you'll learn
- Explore a runtime recommendation system built with neural networks using Analytics Zoo on Apache Spark
- Understand the process for developing and deploying a full end-to-end deep learning-based data analytics workflow, including elements of big data and machine learning in the cloud
Restaurant Brands International
Luyang Wang is a senior manager on the Burger King guest intelligence team at Restaurant Brands International, where he works on machine learning and big data analytics. He’s engaged in developing distributed machine learning applications and real-time web services for the Burger King brand. Previously, Luyang Wang was at Philips Big Data and AI Lab and Office Depot.
Jiao (Jennie) Wang is a software engineer on the big data technology team at Intel, where she works in the area of big data analytics. She’s engaged in developing and optimizing distributed deep learning framework on Apache Spark.
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