Name: [DC] PANCAKE STACK: Spark Recommender +Kafka +Cassandra +Streams +TensorFlow
Start: 2016-06-18T09:00:00-07:00
End: 2016-06-18T17:00:00-07:00
Location: Metro Offices, Ballston

RSVP Here: http://advanced-spark-kafka-tensorflow-washington-dc.eventbrite.com/?discount=ADVANCEDSPARK20

Location: Washington, DC

Metro Offices, Ballston
4601 N Fairfax Drive, 12th Floor
Arlington, VA 22203

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

Cost (Lunch Included)

$199 with 20% ADVANCEDSPARK20 Discount Code!!

(Cost covers office space, food, coffee, power, wifi, air conditioning, audio, video, weekend staff, cloud instances, etc)

Food and Drink

• Lunch: Pizza for various dietary types

Relevant Links

• (http://advancedspark.com)http://pancake-stack.com (http://pancake-stack.com/)

• (http://pancake-stack.com)http://advancedspark.com (http://advancedspark.com/)

• http://pancakebot.com (http://pancakebot.com)

Title

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

http://photos4.meetupstatic.com/photos/event/b/c/9/6/600_448368278.jpeg

PANCAKE STACK

PANCAKE

Presto

Apache Arrow

Apache NiFi

Apache Cassandra

AirFlow

Apache Kafka

ElasticSearch

STACK

Apache Spark

TensorFlow

Algebird

CoreNLP

Kibana

140 Character Summary

Developer of SMACK Stack, Chris Fregly Follows Up With PANCAKE STACK! Global Workshops #ApacheSpark, #TensorFlow http://advancedspark.com

Instructor Bio

Chris Fregly is a Research Scientist at Flux Capacitor AI, a streaming analytics and machine learning startup in San Francisco.

Chris is an Apache Spark Contributor, Netflix Open Source Committer, organizer of the global Advanced Spark and TensorFlow Meetup, and author of the upcoming book, Advanced Spark.

Previously, Chris was an Engineer at Databricks and Netflix - as well as a founding member of the IBM Spark Technology Center.

http://advancedspark.com/img/fregly-300x300.png

http://photos4.meetupstatic.com/photos/event/b/9/7/5/600_449987477.jpeg

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 productionize 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

• Productionize 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

• Anyone who wants to try a 3D-Printed PANCAKE!!

http://photos2.meetupstatic.com/photos/event/b/e/0/5/600_448368645.jpeg

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!

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

And once again, the PANCAKE STACK! :)

http://advancedspark.com/img/pancake-stack.png

Here is the RSVP link again: http://advanced-spark-kafka-tensorflow-washington-dc.eventbrite.com/?discount=ADVANCEDSPARK20

See you all soon!!

Chris Fregly

AI Performance Engineering Meetup (San Francisco, Global)

Technology

Predictive Analytics

High Scalability Computing

Artificial Intelligence Programming

Artificial Intelligence Applications

Apache Spark

Data Science

Machine Learning

TensorFlow

Big Data

Deep Learning

Neural Networks

Artificial Intelligence

Natural Language Processing

Kubernetes

PyTorch

[DC] PANCAKE STACK: Spark Recommender +Kafka +Cassandra +Streams +TensorFlow

Metro Offices, Ballston

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AI Performance Engineering Meetup (San Francisco, Global)

[DC] PANCAKE STACK: Spark Recommender +Kafka +Cassandra +Streams +TensorFlow

AI Performance Engineering Meetup (San Francisco, Global)

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