Berlin Buzzwords 2018: Nick Pentreath – Search and Recommendations: 3 Sides of the Same Coin #bbuzz
Berlin Buzzwords 2019: Nick Pentreath – Open standards for machine learning deployment #bbuzz
Berlin Buzzwords 2018: Felipe Besson – Learning to Rank journey at GetYourGuide: Our Logbook #bbuzz
Berlin Buzzwords 2018: Benoit Hanotte – Profiling and optimizing a Spark job with Babar #bbuzz
Berlin Buzzwords 2018: Philipp Krenn – ElasticSearch (R)Evolution — You Know, for Search ... #bbuzz
Berlin Buzzwords 2018: Giovanni Fernandez-Kincade – Getting Started with Query Understanding #bbuzz
Berlin Buzzwords 2018: Joshua Bacher & Christine Bellstedt – Search suggestions as killer feature
Berlin Buzzwords 2018: Vlad Dolezal – Calculating recommendations based on product images #bbuzz
Berlin Buzzwords 2018: Houston Putman – Relevant Data Analysis: Apache Solr Analytics #bbuzz
Berlin Buzzwords 2018: Doug Turnbull & Tommaso Teofili – The Neural Search Frontier #bbuzz
Berlin Buzzwords 2018: Holden Karau – Working with Tensorflow from the JVM #bbuzz
Productionizing ML Pipelines with PFA by Nick Pentreath
Berlin Buzzwords 2019: Lester Solbakken – Scaling ONNX and TensorFlow model evaluation in search
Berlin Buzzwords 2019: Sophie Watson – Dealing with pain points of recommenders in the real world
Lessons Learned Building an Open Deep Learning Model Exchange with Nick Pentreath (IBM)
Berlin Buzzwords 2018: Uwe Schindler – Apache Lucene and Java 9+ #bbuzz
TUTORIAL: Modern Recommender Systems: from Computing Matrices to Thinking with Neurons
NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Vowpal Wabbit Tutorial
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Using Text Embedding Algorithms in Recomm. Systems
