ODSC Webinar Calendar

ODSC’s free webinar series serves to educate our community on the languages, tools, and topics of AI and Data Science


ODSC West Warm-Up

October 3rd, 2018

starting at 1:00 PM PST

4 speakers from our upcoming ODSC Europe conference
30 minutes sessions



Marc Fridson, Principal Data Scientist at Carnival

Balancing ML Accuracy, Interpretability and Costs When Building a Model

 

Balancing ML Accuracy, Interpretability and Costs When Building a Model

As data scientists we strive to deliver high performance models, but in the real-world the best model possible is not usually the best model for the business. When developing a model if it is not interpretable by the business, you will be unable to get buy in necessary to get your model into production. Additionally, you are always fighting two cost related battles: opportunity cost of delivering a perfect model tomorrow instead of delivering a good one today; operational costs of the most superior model compared to the next best one. This workshop will use real-world coding examples in Python to demonstrate how to be mindful of these constraints when developing your models.

Presenter bio - Marc Fridson, principal data scientist at carnival

Marc Fridson is the Principal Data Scientist of Cross Brand Digital @ Carnival Cruise Line, a Part-Time Lecturer for the Applied Analytics Program Masters Program @ Columbia University and the founder of tech start-up Instant Analytics. Marc has previously worked as a Technology Consultant for Accenture, as an Engineer for the Boeing Company, AVP of Metrics and Reporting for Capital One, and as Manager of Analytics at CB Richard Ellis for JP Morgan Chase’s Real Estate Management. Previous consulting clients include: Morgan Stanley, Capital One, The College Board, Anthem Blue Cross, Verizon and Time Warner Cable. He has helped these companies measure, analyze, and automate their processes through data analysis and by developing technological tools to enable process improvement/automation. He holds a B.S. in Industrial and Systems Engineering from Rutgers University.

Sean Gorman, Head of Technical Product Management at DigitalGlobe

&

Steven Pousty, Developer Relations Lead at DigitalGlobe

How to use Satellite Imagery to be a Machine Learning Mantis Shrimp

How to use Satellite Imagery to be a Machine Learning Mantis Shrimp

In this session we are going to start by showing you how satellite imagery actually allows you to “see” in more bands of color than the mantis (how about 26 bands) – each band is a massive amount of data about the earth. Then we will show you how you can work with this data in Jupyter notebooks to extract all sorts of information about the world. Finally, we will wrap up with how to make ML models using this data, extract features we care about, and then run it through a cloud-based processing model.

Presenter bio - sean gorman, head of technical product management at digitalglobe & steven pousty, developer relations lead at digitalglobe

Sean is the Head of Technical Product Management at DigitalGlobe helping build GBDX and next generation machine learning tools for satellite imagery. Previously he was a founder of Timbr.io – a platform for enabling algorithm reusability and more accessible data science – acquired by DigitalGlobe in 2016. Before starting Timbr.io he was a founder of GeoIQ – a collaborative data and analytics company. GeoIQ was subsequently acquired by ESRI where Sean worked integrating social data and streaming analytics with ESRI’s mapping technologies. Sean has also previously worked in academia serving as a research professor at George Mason University. Sean received his PhD from George Mason University as the Provost’s High Potential Research Candidate, Fisher Prize winner and an INFORMS Dissertation Prize recipient.

Steve is the Developer Relations lead for DIgitalGlobe. He goes around and shows off all the great work the DigitalGlobe engineers do. He can teach you about Data Analysis with Java, Python, PostgreSQL MongoDB, and some JavaScript. He has deep subject area expertise in GIS/Spatial, Statistics, and Ecology. He has spoken at over 50 conferences and done over 30 workshops including Monktoberfest, MongoNY, JavaOne, FOSS4G, CTIA, AjaxWorld, GeoWeb, Where2.0, and OSCON. Before DigitalGlobe, Steve was a developer evangelist for Red Hat, LinkedIn, deCarta, and ESRI. Steve has a Ph.D. in Ecology from University of Connecticut. He likes building interesting applications and helping developers and data scientists do more with spatial data

Michael Mahoney, PhD, Professor at UC Berkeley

Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap

Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap

In this talk we will describe some of the underlying randomized linear algebra techniques. Finally, we’ll describe Alchemist, a system for interfacing between Spark and existing MPI libraries that is designed to address this performance gap. The libraries can be called from a Spark application with little effort, and we illustrate how the resulting system leads to efficient and scalable performance on large datasets. We describe use cases from scientific data analysis that motivated the development of Alchemist and that benefit from this system. We’ll also describe related work on communication-avoiding machine learning, optimization-based methods that can call these algorithms, and extending Alchemist to provide an ipython notebook <=> MPI interface.

Presenter Bio - Michael Mahoney, PhD, Professor at UC Berkeley

Michael Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received him PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he is on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council’s Committee on the Analysis of Massive Data, he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets, and he spent fall 2013 at UC Berkeley co-organizing the Simons Foundation’s program on the Theoretical Foundations of Big Data Analysis.

Speaker 4: TBD

 


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10/3/2018 1:00 PM
America/Los_Angeles
ODSC West Warm-Up

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ODSC West Warm-Up

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