Past Event

End-to-End Streaming ML Recommendation Pipeline Workshop

Learn to build an end-to-end, streaming recommendations pipeline using the latest streaming analytics tools inside a portable, take-home Docker Container in the cloud!

Saturday August 27, 2016
9:00 AM - 5:00 PM (Central Standard Time)
2.03 Classroom

This event has ended.

You’ll learn how to...

  • Create a complete, end-to-end streaming data analytics pipeline

  • Interactively analyze, approximate, and visualize streaming data

  • Generate machine learning, graph & NLP recommendation models

  • Productionize our ML models to serve real-time recommendations

  • Perform a hybrid on-premise and cloud deployment using Docker

  • Customize this workshop environment to your specific use cases



Part 1 (Analytics and Visualizations)

Analytics and Visualizations Overview (Live Demo!)

Verify Environment Setup (Docker)

Notebooks (Zeppelin, Jupyter/iPython)

Interactive Data Analytics (Spark SQL, Hive, Presto)

Graph Analytics (Spark Graph, NetworkX, TitanDB)

Time-series Analytics (Cassandra)

Visualizations (Kibana, Matplotlib, D3)

Approximate Queries (Spark SQL, Redis, Algebird)

Workflow Management (AirFlow)

Part 2 (Streaming and Recommendations)

Streaming and Recommendations Overview (Live Demo!)

Streaming (NiFi, Kafka, Spark Streaming, Flink)

Cluster-based Recommendation (Spark ML, Scikit-Learn)

Graph-based Recommendation (Spark ML, Spark Graph)

Collaborative-based Recommendation (Spark ML)

NLP-based Recommendation (CoreNLP, NLTK)

Geo-based Recommendation (ElasticSearch)

Hybrid On-Premise+Cloud Auto-Scale Deploy (Docker)

Customize the Workshop Environment for Your Use Cases

Target Audience

  • Interest in learning more about the streaming data pipelines that power their real-time machine learning models and visualizations

  • Interest in building more intuition about machine learning, graph processing, natural language processing, statistical approximation techniques, and visualizations

  • Interest in learning the practical applications of a modern, streaming data analytics and recommendations pipeline

  • Anyone who wants to try 3D-printed PANCAKES!!



  • Basic familiarity with Unix/Linux commands

  • Experience in SQL, Java, Scala, Python, or R

  • Basic familiarity with linear algebra concepts (dot product)

  • Laptop with an ssh client and modern browser

  • Every attendee will get their own fully-configured cloud instance running the entire environment

  • At the end of the workshop, you will be able to save and download your environment to your local laptop in the form of a Docker image

End-to-End Streaming ML Recommendation Pipeline Workshop
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