好資源趕緊收藏。
為期三天的 Spark Summit 在美國時間 2018-06-04 ~ 06-06 於舊金山的 Moscone Center 舉行，不少人已經注意到，今年的會議已經更名為 Spark+AI, 去年 12 月份時，Databricks 在他們的博客中就已經提到過，2018 年的會議將包括更多人工智能的內容，某種意義上也代表著 Spark 未來的發展方向。作為大數據領域的頂級會議，Spark Summit 2018 吸引了全球近 2000 位技術大咖參會。本次會議議題超過了170多個，有超過一半的議題為機器學習及深度學習。會議的全部日程請參見：https://databricks.com/sparkaisummit/north-america/schedule。
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GitHub 下載地址：https://github.com/397090770/spark-summit-north-america-2018-06
CSDN 下載：https://download.csdn.net/download/w397090770/10485708 (分卷 1)、https://download.csdn.net/download/w397090770/10484033 (分卷 2)，為了避免伸手黨，CSDN 的文件設置瞭解壓密碼（解壓密碼為不帶www的本博客域名，或關注微信公眾號 iteblog_hadoop 回覆 spark_summit_201806 獲取），共需要 2 積分下載。
本站 FTP 下載：https://www.iteblog.com/sparksummit/
全部可下載的PPT
本博客整理了共 147 個 PPT，已經全部上傳到 GitHub 供大家下載：
（GitHub）：進入GitHub下載本次會議全部PPT(https://github.com/397090770/spark-summit-north-america-2018-06)
1. 99 Problems but Databricks + Apache Spark Ain’t One
2. A Deep Dive into Stateful Stream Processing in Structured Streaming
3. A Machine Learning Approach to Time-Sensitive Data Analysis
4. A Tale of Three Deep Learning Frameworks TensorFlow, Keras, and Deep Learning Pipelines
5. Accelerated Spark on Azure Seamless and Scalable Hardware Offloads in the Cloud
6. Accelerating Data Analysis of Brain Tissue Simulations with Apache Spark
7. Accelerating Inference in the Data Center
8. Accelerating Real Time Analytics with Spark Streaming and FPGAaaS
9. AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Technologies
10. Alchemist An Apache Spark = MPI Interface
11. An End-to-End Spark-Based Machine Learning Stack in the Hybrid Cloud
12. An Update on Scaling Data Science Applications with SparkR in 2018
13. Analytics Zoo - Building Analytics and AI Pipeline for Apache Spark and BigDL
14. Analyzing Blockchain Transactions in Apache Spark
15. Apache Spark Acceleration Using Hardware Resources in the Cloud, Seamlessl
16. Apache Spark and Machine Learning Boosts Revenue Growth for Online Retailers
17. Apache Spark at Apple
18. Apache Spark Based Hyper-Parameter Selection and Adaptive Model Tuning for Deep Neural Networks
19. Apache Spark Data Source V2
20. Apache Spark for Library Developers
21. Apache Spark-Based Stratification Library for Machine Learning Use Cases
22. Apply Hammer Directly to Thumb; Avoiding Apache Spark and Cassandra AntiPatterns
23. Automated Debugging of Big Data Analytics in Apache Spark Using BigSift
24. Automating and Productionizing Machine Learning Pipelines for Real-Time Scoring
25. Automobile Route Matching with Dynamic Time Warping Using PySpark
26. Avoiding Performance Potholes - Scaling Python for Data Science on Spark
27. Azure Databricks Customer Experiences and Lessons
28. Bighead - Airbnb’s End-to-End Machine Learning Platform
29. Bring Your Own Models—Machine Learning as a Service
30. Bringing an AI Ecosystem to the Domain Expert and Enterprise AI Developer
31. Building a Scalable Record Linkage System with Apache Spark, Python 3, and Machine Learning
32. Building Deep Reinforcement Learning Applications on Apache Spark with Analytics Zoo using BigDL
33. Building Intelligent Applications, Experimental ML with Uber’s Data Science Workbench
34. Building Machine Learning Algorithms on Apache Spark Scaling Out and Up
35. Building Real-Time Data Pipeline for Diabetes Medication Recommender System Using Databricks
36. Cardinality Estimation through Histogram in Apache Spark 2.3
37. Cloud Computing Was Built
for Web Developers—What Does v2 Look Like for Deep Learning38. Cloud Cost Management and Apache Spark
39. Cognitive Database An Apache Spark-Based AI-Enabled Relational Database System
40. Conquering Hadoop and Apache Spark with Operational Intelligence
41. Continuous Processing in Structured Streaming
42. Conversational Artificial Intelligence
43. Create a Loyal Customer Base by Knowing Their Personality Using AI-Based Personality Recommendation Engine
44. Data Science and Enterprise Engineering
45. Deep Credit Risk Ranking
46. Deep Dive into Spark SQL with Advanced Performance Tuning
47. Deep Learning for Domain-Specific Entity Extraction from Unstructured Text
48. Deep Learning for Natural Language Processing Using Apache Spark and TensorFlow
49. Deep Learning for Recommender Systems
50. Deep Learning-Based Opinion Mining for Bitcoin Price Prediction
51. Deploying and Monitoring Heterogeneous Machine Learning Applications
52. Deploying MLlib for Scoring in Structured Streaming
53. Deploying Real-Time Decision Services Using Redis
54. Detecting Mobile Malware with Apache Spark
55. Distributed Inference on Large Datasets Using Apache MXNet and Apache Spark
56. DLoBD An Emerging Paradigm of Deep Learning Over Big Data Stacks
57. Dynamic Class-Based Spark Workload Scheduling and Resource Using YARN
58. Dynamic Healthcare Dataset Generation, Curation & Quality with PySpark
59. Dynamic Priorities for Apache Spark Application’s Resource Allocations
60. Efficiently Triaging CI Pipelines with Apache Spark - Mixing 52 Billion EventsDay of Streaming with 40 TBHour of Batch Processing
61. Enabling Composition in Distributed Reinforcement Learning with Ray RLlib
62. Enterprise Data Governance and Compliance at Scale
63. Extending Apache Spark APIs Without Going Near Spark Source or a Compiler
64. Extending Spark SQL API with Easier to Use Array Types Operations
65. Fact Store at Scale for Netflix Recommendations
66. Fiducial Marker Tracking Using Machine Vision
67. Flare and TensorFlare Native Compilation for Spark and TensorFlow Pipelines
68. From Genomics to NLP – One Algorithm to Rule Them All
69. From Prototyping to Deployment at Scale
70. HIPAA Compliant Deployment of Apache Spark on AWS
71. Horovod Uber’s Open Source Distributed Deep Learning Framework for TensorFlow
72. How Apache Spark Changed the Way We Hire People
73. How Azure Databricks helped make IoT Analytics a reality
74. How Neural Networks See Social Networks
75. How to Rebuild an End-to-End ML Pipeline with Databricks and Upwork
76. How to Use Millions of Mobile Activity Logs to Understand Our Customers, in Real Time
77. Hunt For Lunar Ice AI Lunar Crater Detector
78. Image Similarity Detection at Scale Using LSH and Tensorflow
79. Implementing AutoML Techniques at Salesforce Scale
80. Insights from Building the Future of Drug Discovery with Apache Spark
81. Integrating Existing C++ Libraries into PySpark
82. Interactive Deep Learning in Cloud via MMLSpark
83. Large Scale Feature Aggregation Using Apache Spark
84. Large Scale Fuzzy Name Matching with a Custom ML Pipeline in Batch and Streaming
85. Large-Scaled Telematics Analytics in Apache Spark
86. Lightning-Fast Analytics for Workday Transactional Data
87. Machine Learning for the Apache Spark Developer
88. MacroBase Efficient Explanation On Big Data
89. Managing Thousands of Spark Workers in Cloud Environment
90. Matchmaking Audiences to Content
91. Meltdown, Spectre and Apache Spark™ Performance
92. Merchant Churn Prediction Using SparkML at PayPal
93. Metrics-Driven Tuning of Apache Spark at Scale
94. Migrating Apache Hive Workload to Apache Spark - Bridge the Gap
95. Model Parallelism in Spark ML Cross-Validation
96. Moment-Based Estimation for Hierarchical Models in Apache Spark
97. Moving eBay’s Data Warehouse Over to Apache Spark – Spark as Core ETL Platform at eBay
98. Near Real-Time Netflix Recommendations Using Apache Spark Streaming
99. Nouns are Better than N-Grams
100. Operation Tulip - Using Deep Learning Models to Automate Auction Processes
101. Operationalizing Edge Machine Learning with Apache Spark
102. Operationalizing Machine Learning—Managing Provenance from Raw Data to Predictions
103. Optimizing Apache Spark Throughput Using Intel Optane and Intel Memory Drive Technology
104. Overview of Apache Spark 2.3 - What’s New
105. Pandas UDF-Scalable Analysis with Python and PySpark
106. Pharmacy Claims Fraud Detection Using Apache Spark
107. Predictive Maintenance at the Dutch Railways
108. Productionizing H2O Models with Apache Spark
109. Productionizing Spark ML Pipelines with the Portable Format for Analytics
110. Programming by Examples
111. Real-Time Attribution with Structured Streaming and Databricks Delta
112. Real-Time In-Flight Drone Route Optimization with Apache Spark
113. Risk Management Framework Using Intel FPGA, Apache Spark, and Persistent RDDs
114. Scalable Monitoring Using Prometheus with Apache Spark Clusters
115. Scale a Near Real-Time AI System by 4X and Beyond with Apache Spark
116. Scaling Machine Learning at Booking.com with H2O Sparkling Water and FeatureStore
117. Separating Hype from Reality in Deep Learning
118. Serverless Machine Learning on Modern Hardware Using Apache Spark
119. SOS - Optimizing Shuffle IO
120. Spark + AI Helps the FDA Protect the Nation
121. Spark from Notebook to Cloud Native Application
122. Spark NLP Extending Spark ML to Deliver Fast, Scalable & Unified Natural Language Processing
123. Spark SQL Adaptive Execution Unleashes The Power of Cluster in Large Scale
124. Sparser-Faster Parsing of Unstructured Data Formats in Apache Spark
125. State of the Art Natural Language Processing
126. Strava Labs - Exploring a Billion Activity Dataset from Athletes with Apache Spark
127. Streaming Trend Discovery Real-Time Discovery in a Sea of Events
128. The Rise Of Conversational AI with David Low
129. Theory Meets Reality—Large Scale Frequent Pattern Mining with Apache Spark in the Real World
130. Threat Detection and Response at Scale
131. Time Series Forecasting Using Recurrent Neural Network and Vector Autoregressive Model
132. Training neural networks with low precision floats
133. Transparent GPU Exploitation on Apache Spark
134. TuneIn How to Get Your HadoopSpark Jobs Tuned While You’re Sleeping
135. Understanding Parallelization of Machine Learning Algorithms in Apache Spark™
136. Using AI to Build a Self-Driving Query Optimizer
137. Using AI to Deliver a Device as a Service
138. Using Apache Spark to Predict Installer Retention from Messy Clickstream Data
139. Using Apache Spark to Tune Spark
140. Using BigDL on Apache Spark to Improve the MLS Real Estate Search Experience at Scale
141. Using Spark-Solr at Scale Productionizing Spark for Search
142. Virtualizing Apache Spark and Machine Learning
143. When Apache Spark meets TiDB
144. Which Data Broke My Code Inspecting Spark Transformations
145. Whirlpools in the Stream
146. Why is My Stream Processing Job Slow
147. Zipline - Airbnb’s Machine Learning Data Management Platform
轉載本文請加上：轉載自過往記憶（https://www.iteblog.com/）
本文鏈接: 【Spark Summit North America 201806 全部PPT下載[共147個]】（https://www.iteblog.com/archives/2379.html）
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