为期三天的 Spark+AI Summit Europe 于 2018-10-02 ~ 04 在伦敦举行，一如往前，本次会议包含大量 AI 相关的议题，某种意义上也代表着 Spark 未来的发展方向。作为大数据领域的顶级会议，Spark+AI Summit Europe 2018 吸引了全球大量技术大咖参会，本次会议议题超过了140多个。会议的全部日程请参见：https://databricks.com/sparkaisummit/europe/schedule。注意，本次会议的 PPT 全部由程序下载，如有问题请联系我。

如果想及时了解Spark、Hadoop或者Hbase相关的文章，欢迎关注微信公共帐号：iteblog_hadoop

• Apache Spark 接下来的发展方向
• 机器学习的最佳实践
• 最新的深度学习和机器学习框架
• 统一分析平台将数据和AI结合起来
• 典型的人工智能案例
• 在各种应用程序中大规模使用Apache Spark
• Structured Streaming 和 Continuous Applications

下载途径

GitHub 下载地址：https://github.com/397090770/spark-ai-summit-europe-2018-10
CSDN 下载：https://download.csdn.net/download/w397090770/10717551，为了避免伸手党，CSDN 的文件设置了解压密码，关注微信公众号 iteblog_hadoop 回复 spark_summit_eu_2018 获取。
过往记忆 FTP 下载：https://www.iteblog.com/sparksummit2018/

全部可下载的PPT

下面 PPT 全部可以通过本站 CDN 在线查看。

• MLflow: Infrastructure for a Complete Machine Learning Life Cycle
• FP&A with Spreadsheets and Spark
• Learning to Rank Datasets for Search
• An Introduction to Higher Order Functions in Spark SQL
• Spark Schema For Free
• Real-Time Predictions on the Factory Floor
• A Microservices Framework for Real-Time Model Scoring Using Structured Streaming
• Lessons Learned Developing and Managing High Volume Apache Spark Pipelines in Production
• Experience of Running Spark on Kubernetes on OpenStack for High Energy Physics Workloads
• Hudi: Large-Scale, Near Real-Time Pipelines at Uber
• IBM Developer Model Asset Exchange
• CI/CD for Machine Learning
• Time Series Forecasting Using Recurrent Neural Network and Vector Autoregressive Model: When and How
• A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & PyTorch
• Distributed Deep Learning with Apache Spark and TensorFlow
• Geospatial Analytics at Scale with Deep Learning and Apache Spark
• Building an Implicit Recommendation Engine with Spark
• Time-Series Anomaly Detection in Plaintext Using Apache Spark
• Great Models with Great Privacy: Optimizing ML and AI Under GDPR
• Spark SQL Catalyst Code Optimization using Function Outlining
• Using Azure Databricks, Structured Streaming, and Deep Learning Pipelines to Monitor 1,000+ Solar Farms in Real Time
• Neo4j Morpheus: Interweaving Documents, Tables and and Graph Data in Spark
• Towards a Unified Data Analytics Optimizer
• Sparklens: Understanding the Scalability Limits of Spark Applications
• Towards Writing Scalable Big Data Applications
• Designing and Building Next Generation Data Pipelines at Scale with Structured Streaming
• Efficient Spark Analytics on Encrypted Data
• Apache Spark Based Reliable Data Ingestion in Datalake
• Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Love to Scale
• Correctness and Performance of Apache Spark SQL
• Learning to Rank with Apache Spark: A Case Study in Production Machine Learning
• Machine Learning for AdTech in Action
• ML at the Edge: Building Your Production Pipeline with Apache Spark and TensorFlow
• Productionizing a Machine Learning System at a Large Australian Telco
• FlowSpec—Apache Spark Pipelines in Production
• Using Deep Learning in Production Pipelines to Predict Consumers’ Interest
• Pitfalls of Apache Spark at Scale
• Accelerating AI Results in the Enterprise
• Attribution Done Right
• Running Spark In Production in the Cloud is Not Easy
• Avoiding Log Data Overload in a CI/CD System: Streaming 190 Billion Events and Batch Processing 40 TB/Hour
• HEP Data Processing with Apache Spark
• Streaming Random Forest Learning in Spark and StreamDM
• Spark-ITS: Indexing for Large-Scale Time Series Data on Spark
• Spark-MPI: Approaching the Fifth Paradigm
• Smart Searching Through Trillion of Research Papers with Apache Spark ML
• Reforming Traditional Machine Learning Algorithms with Spatio-Temporal Analytics Capability for Big Data
• Rejecting the Null Hypothesis of Apathetic Retweeting of US Politicians and SPLC-defined Hate Groups in the 2016 US Presidential Election
• Building Streaming Recommendation Engines on Apache Spark
• Interaction-Based Feature Extraction: How to Convert Your Users’ Activity into Valuable Features
• Three Stats Pitfalls Facing the New Data Scientist
• Deploying Python Machine Learning Models with Apache Spark
• Patterns for Successful Data Science Projects
• Analytical DBMS to Apache Spark Auto Migration Framework
• Apache Spark for Library Developers
• Apache Spark on K8S and HDFS Security
• Deep Dive into Stateful Stream Processing in Structured Streaming
• Deep Reality Simulation for Automated Poacher Detection
• Detecting Mobile Malware with Apache Spark
• Lambda Architecture in the Cloud with Azure Databricks
• ABRIS: Avro Bridge for Apache Spark
• Modular Apache Spark: Transform Your Code in Pieces
• Extending Structured Streaming Made Easy with Algebra
• Unafraid of Change: Optimizing ETL, ML, and AI in Fast-Paced Environments
• Predicting Social Engagement of Social Images with Deep Learning
• Powering NLU Engine with Apache Spark to Communicate with World
• An AI Use Case: Market Event Impact Determination via Sentiment and Emotion Analysis
• AI and Machine Learning for the Connected Home
• Intelligence Driven User Communications at Scale
• Project Hydrogen: Unifying State-of-the-Art AI and Big Data in Apache Spark
• Recurrent Neural Networks for Recommendations and Personalization
• How We Used Databricks, MLeap, and Kubernetes to Productionize Spark ML Faster
• CardioAI: Automated Medical Diagnosis from MRI and Patient Data Using Deep Learning
• Open ETL for Real-Time Decision Making
• AI That Cares About Your Broadband Connection
• Road to Enterprise Architecture for Big Data Applications: Mixing Apache Spark with Singletons, Wrapping, and Facade
• Transforming Devon’s Data Pipeline with an Open Source Data Hub—Built on Databricks
• Scaling Advanced Analytics at Shell
• Using Apache Spark Structured Streaming on Azure Databricks for Predictive Maintenance of Coordinate Measuring Machines
• Lessons from the Field, Episode II: Applying Best Practices to Your Apache Spark Applications
• Azure Databricks—Apache Spark as a Service
• From “All-at-Once, Once-a-Day” to “A-Little-Each-Time, All-the-Time”
• Deep Reality Simulation for Automated Poacher Detection
• Designing a Horizontally Scalable Event-Driven Big Data Architecture with Apache Spark
• CERN’s Next Generation Data Analysis Platform with Apache Spark
• PyTorch - an ecosystem for deep learning
• Co-op’s Transformation from Brick and Mortar to AI with Databricks
• The Future of Media and Retail Measurement: How Nielsen Evolved into an AI-First Company
• The Evolution of the Fashion Retail Industry in the Age of AI
• The Future of Healthcare with Big Data and AI
• Developing for the Intelligent Cloud and Intelligent Edge
• Moving Towards AI at Shell
• The Power of Unified Analytics
• Accelerating Production Machine Learning with MLflow
• Unifying State-of-the-Art AI and Big Data in Apache Spark