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: Joshua Bacher & Christine Bellstedt – Search suggestions as killer feature
Berlin Buzzwords 2018: Benoit Hanotte – Profiling and optimizing a Spark job with Babar #bbuzz
Berlin Buzzwords 2018: Uwe Schindler – Apache Lucene and Java 9+ #bbuzz
Berlin Buzzwords 2018: Houston Putman – Relevant Data Analysis: Apache Solr Analytics #bbuzz
Berlin Buzzwords 2018: Giovanni Fernandez-Kincade – Getting Started with Query Understanding #bbuzz
Berlin Buzzwords 2018: Vlad Dolezal – Calculating recommendations based on product images #bbuzz
Lessons Learned Building an Open Deep Learning Model Exchange with Nick Pentreath (IBM)
Berlin Buzzwords 2018: Holden Karau – Working with Tensorflow from the JVM #bbuzz
Berlin Buzzwords 2019: Lester Solbakken – Scaling ONNX and TensorFlow model evaluation in search
Productionizing ML Pipelines with PFA by Nick Pentreath
Berlin Buzzwords 2019: Sophie Watson – Dealing with pain points of recommenders in the real world
Patricio Cofre | NetConfCL v2018 | Accelerating Artificial Intelligence with WinML and ONNX
Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model
Doug Turnbull - Hacking Lucene for Custom Search Results
Using Text Embedding Algorithms in Recomm. Systems
How Docker supports Machine Learning Models Deployment & Productionization - Use Case
TUTORIAL: Modern Recommender Systems: from Computing Matrices to Thinking with Neurons
Recommendations in the real world by Sophie Watson
