addressalign-toparrow-leftarrow-rightbackbellblockcalendarcameraccwcheckchevron-downchevron-leftchevron-rightchevron-small-downchevron-small-leftchevron-small-rightchevron-small-upchevron-upcircle-with-checkcircle-with-crosscircle-with-pluscrossdots-three-verticaleditemptyheartexporteye-with-lineeyefacebookfolderfullheartglobegmailgooglegroupsimageimagesinstagramlinklocation-pinm-swarmSearchmailmessagesminusmoremuplabelShape 3 + Rectangle 1outlookpersonJoin Group on CardStartprice-ribbonShapeShapeShapeImported LayersImported LayersImported Layersshieldstartickettrashtriangle-downtriangle-uptwitteruserwarningyahoo

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

RSVP (FREE) Here:  https://end-to-end-streaming-recommendations-spark-bangalo.eventbrite.com/ 

This will be part of a join meetup with the Bangalore Apache Spark Meetup!  

**SPACE IS LIMITED, SIGN UP SOON!**

Location 

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


Relevant Links 

http://pipeline.io 

https://github.com/fluxcapacitor/pipeline.io 

https://github.com/fluxcapacitor/pipeline/wiki 


Title

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

PANCAKE STACK

PANCAKE

Presto

Apache Arrow

Apache NiFi

Apache Cassandra

AirFlow

Apache Kafka

ElasticSearch


STACK

Apache Spark

TensorFlow

Algebird

CoreNLP

Kibana


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 http://pipeline.io


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


Prerequisites

• 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:  https://end-to-end-streaming-recommendations-spark-bangalo.eventbrite.com/


See you all soon!!

Join or login to comment.

Sign up

Meetup members, Log in

By clicking "Sign up" or "Sign up using Facebook", you confirm that you accept our Terms of Service & Privacy Policy