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Scale By the Bay 2019
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#1
How to Eliminate Surprises In Your Data
#2
Enabling real time querying of Data using Apache Druid, Flink and Kafka
#3
Apache Flink 2.0: Unified Enterprise Data Processing System and Beyond
#4
Vectorized Query Processing for CPUs and GPUs using Apache Arrow
#5
Scaling Financial Automation on TypeBus
#6
End-to-End ML Pipelines with KubeFlow and TensorFlow Extended (TFX)
#7
From datasets to tables in a multitenant data lake
#8
Reliable, High Scale Tensorflow Inference Pipelines at Twitter
#9
Was He Wright All Along? Software After Moore's Law
#10
Large Scale On-Demand Low-Latency Near Real-Time Predictions
#11
Next-generation frameworks for Large-scale Machine Learning
#12
Swift for TensorFlow: Machine Learning with No Boundaries
#13
Introduction to geospatial analysis for uninitiated SQL Data Engineer
#14
Human-Centric ML Infrastructure at Netflix
#15
Build Your Own ML Data Feedback Loop
#16
Weld: An Optimizing Runtime for High Performance Data Analytics
#17
Lessons Learnt Building Domain Specific NLP Pipelines
#18
Ludwig, a Code-Free Deep Learning Toolbox
#19
Machine Learning's Missed Opportunity in Visual Data Management
#20
Discovering Your Model's Known Unknowns and Unknown Unknowns
#21
Hack Weekend: ML models on mobile
#22
machine learning and mobile
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Ricardo Aravena
Muktesh Mishra
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