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[Bangalore] Build End-to-End Spark ML Recommender Engine with Kafka, TensorFlow!

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  • IMS Health

    Embassy Tech Square, Omega Marathahalli, Sarjapur Outer Ring Road Kadubeesanahalli, Bangalore (map)

    12.935099 77.693474

  • RSVP (FREE) Here:



    IMS Health
    Embassy Tech Square, Omega Marathahall
    Sarjapur Outer Ring Road
    Kadubeesanahalli, Bangalore[masked]

    Relevant Links


    Building a Complete, End-to-End, Streaming Data Analytics Pipeline and Recommendation Engine with the PANCAKE STACK including TensorFlow!!




    Apache Arrow

    Apache NiFi

    Apache Cassandra


    Apache Kafka



    Apache Spark





    Agenda (Full Day)

    Part 1 (Analytics and Visualizations)

    • Analytics and Visualizations (Live Demo!)

    • Verify Environment Setup (Docker Machine)

    • 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, Redis, Algebird)

    • Workflow Management (Airflow)

    Part 2 (Streaming and Recommendations)

    • Streaming and Recommendations (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 and Save Environment for Your Use Cases

    140 Character Summary

    Developer of SMACK Stack, Chris Fregly Follows Up With PANCAKE STACK!  Global Workshops #ApacheSpark, #TensorFlow

    Workshop Description

    The goal of this workshop is 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.

    First, we create a data pipeline to interactively analyze, approximate, and visualize streaming data using modern tools such as Apache Spark, NiFi, Kafka, Zeppelin, iPython, and ElasticSearch.

    Next, we extend our pipeline to use streaming data to generate personalized recommendation models from using popular machine learning, graph, and natural language processing techniques such as collaborative filtering, clustering, and topic modeling.

    Lastly, we production-ize our pipeline and serve live recommendations to our users!

    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

    • Production-ize our ML models to serve recommendations in real-time

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

    • Customize this workshop environment to your specific use cases

    Target Audience

    • Data Scientists and Analysts interested in learning more about the streaming data pipelines that power their real-time machine learning models and visualizations

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

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


    • Basic familiarity with Unix/Linux commands

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

    • Basic familiarity with linear algebra concepts like dot product and matrix multiply

    • Laptop with modern browser and ssh capabilities (Mac OSX, Windows, or Linux)

    Note: We provide a cloud instance for each attendee to access from your laptop.

    At the end of the workshop, you will be able to save your work and copy it locally to your laptop to use at home or at the office!

    Instructor Bio

    Chris Fregly is a Research Engineer at PipelineIO, a streaming analytics and machine learning startup in San Francisco. 

    Chris Fregly is a Research Scientist at PipelineIO - a Streaming Machine Learning and Artificial Intelligence Startup in San Francisco. 

    Chris is an Apache Spark Contributor, Netflix Open Source Committer, Founder of the Global Advanced Spark and TensorFlow Meetup, Author of the upcoming book, Advanced Spark, and Creator of the upcoming O'Reilly video series, Deploying and Scaling Distributed TensorFlow in Production.

    Previously, Chris was a Distributed Systems Engineer at Netflix, Data Solutions Engineer at Databricks, and a Founding Member of the IBM Spark Technology Center in San Francisco.

    And once again, the PANCAKE STACK!  :)

    Here is the RSVP (FREE) link:

    See you all soon!!

Join or login to comment.

  • Hema

    Will this meet-up be streamed online. Can we remote login to join this meetup?

    5 days ago

  • Jaikant S.

    I am based on Hyderabad, is there a way to remote join ?
    Is this event planned for Hyderabad sometimes soon?

    December 2

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