What You Missed on Day Two of AnacondaCON 2018

 

What a day! On Tuesday we got started bright and early, then partied our way into the night. Here are some highlights from Day Two of AnacondaCON 2018.

Opening Keynote: John Kim


John Kim, President of HomeAway, kicked things off for us with a personal, touching keynote on Love in the Age of Machine Learning. As technology continues to replace humans, many people are feeling unprepared for the drastic changes that lie ahead. John noted that these changes are forcing us to become even more human, and explored how our humanity and the ability to love and care for others will be our competitive advantage in this “age of irony.”

GPU-Accelerating UDFs in PySpark

The new bottleneck in big data technologies is often the processing power of the CPU. Joshua Patterson and Keith Kraus of NVIDIA’s Applied Solutions Engineering team joined us to share how they were able to GPU-accelerate UDFs in PySpark using OS technologies such as Numba and PyGDF, and all the lessons they learned in the process.

Learning from the Learners

Jonathan Cornelissen and Nick Carchedi from DataCamp—the foremost provider of data science education with 2.3 million learners—joined us to demonstrate how they use data to inform them what and how to teach, as well as how to continuously improve their product and course curriculum.


Recreating the Clueless Outfit Matcher

Paige Bailey of Microsoft lived up to her twitter handle—@DynamicWebPaige—by presenting a fun and engaging tutorial in which she used image recognition to take an existing deep learning model and adapt it to a specialized domain—guessing the style of an article of clothing! Paige wowed the crowd and kept us entertained by recreating the outfit matcher from the movie Clueless using Transfer Learning ML. Choose a bad outfit? As if!

Roadmapping Apache Arrow

Wes McKinney swung by to talk to a packed house about the ongoing community efforts building Apache Arrow, an open source project for high-performance analytics and data interoperability. Wes explained the rationale for the project and the development that has occurred thus far, then took a look at the future roadmap for Arrow as it relates to data science and machine learning applications.

PARTY TIME!


Here at Anaconda, we know how to party Austin-style! After a long day of amazing talks, it was time to unwind with our AnacondaCON Carne offsite party. Held at the iconic Fair Market just east of downtown, our party featured authentic Austin cuisine from local food trucks, an open bar, a s’mores station, and music from our favorite local DJ.


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