Check out this presentation which is focussed on ML Systems at Spotify including less obvious pitfalls, which have caused troubles at Spotify. This talk assumes a certain level of familiarity with ML: You’ll get the most out of if you’ve some experience with applied ML, ideally on production systems.
To: All Aspiring Data Scientists: I’ve exchanged messages with many of you about how to land that first job as a Data Scientist. Landing the first job is indeed hard with all the competition, but if you had a road-map for exactly what to do that was created by a Director of Data Science, who’s been […]
FLINK FORWARD Berlin, from September. Keynote from Xiaowei Jiang, Senior Director at Alibaba. “Bring Flink to Large Scale Production”
From PyData Amsterdam, April 2017. Giovanni Lanzani gives a talk on the Data Science Process and where things can go wrong.
VIDEO | Join this 6-module session which will take an insightful approach for anyone who wants to get started with deep learning and give intuition to explore the areas of DNN(Deep Neural Networks) April 11th or OnDemand.
This million dollar Kaggle contest is structured into two rounds. In the qualifying round, opening today, you’ll be building a model to improve the Zestimate residual error. The top 100 ranking teams in this round will advance to the final round.
MLTrain is coming back to New York City for another training event. Nick Vasiloglou and Alex Dimakis will cover several Machine Learning and TensorFlow topics. We have prepared a 2 day curriculum. You can register for each day individually or for both days. The space is offered by Ebay! When: 6/2-6/3 2017, 9:00am to 2:00pm
Video of keynote from Strata + Hadoop World in San Jose 2017. Machine learning at Google with Rob Craft (Google)
(Reposted due to popular demand) Another great video from Josh Wills. Josh is Sr. Director of Data Science at Cloudera and has a gift for making fairly complicated technology explanations very digestible to the novice and intermediary techie.
What I most love about this video is how Josh explains -very clearly – the issue of translating analytics Machine Learning on a large set of data records (many individuals) and making it work well in a “real life” production environment on a single individual (think eCommerce). Watch Video
“Edward”: A library for probabilistic modeling, inference, and criticism Abstract https://www.meetup.com/NYC-Machine-Learning/events/236943279/ http://www.zentation.com/viewer2/webcast/NAPNgdDUBF/Dustin-Tran—Spotify-Talk-(2017-01-19) Probabilistic modeling is a powerful approach for analyzing empirical information. In this talk, I will provide an overview of Edward, a software library for probabilistic modeling. Formally, Edward is a probabilistic programming system built on computational graphs,supporting compositions of both models and inference […]