Spark+AI Summit Europe 2019 高清视频下载[共135个]

为期三天的 SPARK + AI SUMMIT Europe 2019 于 2019年10月15日-17日荷兰首都阿姆斯特丹举行。数据和 AI 是需要结合的,而 Spark 能够处理海量数据的分析,将 Spark 和 AI 进行结合,无疑会带来更好的产品。Spark+AI Summit Europe 2019 是欧洲最大的数据和机器学习会议,大约有1700多名数据科学家、工程师和分析师参加此次会议。本次会议的提议包括了Apache Spark™、TensorFlow、MLflow 、 PyTorch、Delta Lake、 MLflow 以及 Koalas 等开源技术的最新进展,以及在现实世界中部署人工智能的最佳实践。 会议的全部日程请参见:https://databricks.com/sparkaisummit/europe/schedule

Spark+AI Summit Europe 2019


  • Apache Spark, Delta Lake, MLflow, 以及 Koalas 的未来规划;
  • 流行的深度学习和机器学习框架的最新发展;
  • 使用 MLflow 管理机器学习生命周期
  • 构建大规模可靠数据管道的技巧;
  • 真实可靠的人工智能案例。


CSDN 下载:由于 CSDN 限制,文件分为两个上传,分卷一:https://download.csdn.net/download/w397090770/11950956,分卷二:https://download.csdn.net/download/w397090770/11950964。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 8424 获取。
视频下载(共135个) => 链接: https://pan.baidu.com/s/1WtbPmfn2dOfXWVSluSWRAA
全部 PPT => 链接: 链接:https://pan.baidu.com/s/1Obmh7fb5YaQV0u8R_kURTQ,具体可以参见 Spark+AI Summit Europe 2019 PPT 下载[共98个]
密码请关注微信公众号 iteblog_hadoop 回复 8424 获取。



  • 1、Building Data Intensive Analytic Application on Top of Delta Lakes
  • 2、Near Real Time Data Warehousing with Apache Spark and Delta Lake
  • 3、Petabytes, Exabytes, and Beyond: Managing Delta Lakes for Interactive Queries at Scale
  • 4、Migrating Apache Spark ML Jobs to Spark + Tensorflow on Kubeflow
  • 5、Allies and Adversaries : Explaining Model Reasoning via Contrasting Proximal Prototypes
  • 6、Physical Plans in Spark SQL
  • 7、Downscaling: The Achilles heel of Autoscaling Apache Spark Clusters
  • 8、Astronomical Data Processing on the LSST Scale with Apache Spark
  • 9、Assessing Graph Solutions for Apache Spark
  • 10、AI Scalability for the Next Decade
  • 11、Encrypted Computation in Apache Spark
  • 12、Streaming Analytics for Financial Enterprises
  • 13、.NET for Apache Spark
  • 14、Making Zeppelin and Apache Spark Enjoyable
  • 15、Spark SQL Bucketing at Facebook
  • 16、Koalas: Making an Easy Transition from Pandas to Apache Spark
  • 17、Migrating Hadoop Analytics to Spark in the Cloud Without Disruption
  • 18、Spark Operator—Deploy, Manage and Monitor Spark clusters on Kubernetes
  • 19、Updates from Project Hydrogen: Unifying State of the Art AI and Big Data in Apache Spark
  • 20、Building Resilience in Data Science Processes
  • 21、Getting Started Contributing to Apache Spark
  • 22、Internals of Speeding up PySpark with Arrow
  • 23、Databricks Delta Lake and Its Benefits
  • 24、Asynchronous Hyperparameter Optimization with Apache Spark
  • 25、Apache Spark Side of Funnels
  • 26、Physical Plans in Spark SQL
  • 27、Accelerating Astronomical Discoveries with Apache Spark
  • 28、Accelerating Apache Spark with Intel QuickAssist Technology
  • 29、Data Engineers: Stop Hand Coding and Start Accelerating Your Analytics Projects
  • 30、No REST till Production – Building and Deploying 9 Models to Production in 3 weeks
  • 31、Stream Processing: Choosing the Right Tool for the Job
  • 32、Vectorized R Execution in Apache Spark
  • 33、Extending Spark SQL 2 4 with New Data Sources Live Coding Session
  • 34、A Recommender Story: Improving Backend Data Quality While Reducing Costs
  • 35、Deep Learning Pipelines for High Energy Physics using Apache Spark with Distributed Keras
  • 36、Zipline—Airbnb’s Declarative Feature Engineering Framework
  • 37、Fuel Your Apache Spark Analytics Using Intel Optane DC Persistent Memory
  • 38、Extending Spark Graph for the Enterprise with Morpheus and Neo4j
  • 39、Building a Modern FinTech Big Data Infrastructure
  • 40、Extending Spark SQL 2 4 with New Data Sources
  • 41、How to Tune and Optimize the Performance of Apache Spark Data Pipelines
  • 42、Designing ETL Pipelines with Structured Streaming and Delta Lake
  • 43、Spark Plus AI:ML on AWS
  • 44、Introduction to TensorFlow 2.0
  • 45、Improving Apache Spark by Taking Advantage of Disaggregated Architecture
  • 47、Accelerating Real Time Video Analytics on a Heterogenous CPU + FPGA Platform
  • 48、Building a Knowledge Graph with Spark and NLP: How We Recommend Novel Drugs to our Scientists
  • 49、Apache Spark AI Use Case in Telco: Network Quality Analysis and Prediction
  • 50、Best Practices for Building and Deploying Data Pipelines in Apache Spark
  • 51、Implementing a Reliable Data Lake with Databricks Delta and the AWS Ecosystem
  • 52、Driver Location Intelligence at Scale using Apache Spark, Delta Lake, and MLflow on Databricks
  • 53、Building A Feature Factory Daniel Tomes Databricks
  • 54、Graph Features in Spark 3 0 Integrating Graph Querying and Algorithms in Spark Graph
  • 55、Scalable Time Series Forecasting and Monitoring using Apache Spark and ElasticSearch
  • 56、Modern ETL Pipelines with Change Data Capture Thiago Rigo GetYourGuide
  • 57、Deep Learning with DL4J on Apache Spark: Yeah it’s Cool, but are You Doing it the Right Way
  • 58、Lessons Learned Replatforming A Large Machine Learning Application To Apache Spark
  • 59、Retrieving Visually Similar Products for Shopping Recommendations using Spark and Tensorflow
  • 60、How to Automate Performance Tuning for Apache Spark
  • 61、Data Reproducibility, Audits, Immediate Rollbacks, and Other Applications of Time Travel
  • 62、Transforming AI with Graphs: Real World Examples using Spark and Neo4j
  • 63、 MEET Women in Unified Data Analytics Lunch
  • 64、The Parquet Format and Performance Optimization Opportunities
  • 65、Data Warehousing with Spark Streaming at Zalando
  • 66、Building Data Intensive Analytic Application on Top of Delta Lakes(2)
  • 67、Automating Loss Prevention Using NLP with FastAI on Azure Databricks
  • 68、Making Homes Efficient and Comfortable Using AI and IoT Data
  • 69、Refactoring Apache Spark to Allow Additional Cluster Managers
  • 70、Continuous Deployment for Deep Learning
  • 71、Improving Apache Spark Downscaling
  • 72、Cosmos DB Real time Advanced Analytics Workshop
  • 73、Lessons Learned from Using Spark for Evaluating Road Detection
  • 74、ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scale Storage and Analytics
  • 75、Managing the Complete Machine Learning Lifecycle with MLflow
  • 76、Maps and Meaning Graph based Entity Resolution in Apache Spark & GraphX
  • 77、Using PySpark to Scale Markov Decision Problems for Policy Exploration
  • 78、Deep Anomaly Detection from Research to Production Leveraging Spark and Tensorflow
  • 79、Commercial Analytics at Scale in Pharma- From Hackathon to MVP with Azure Databricks
  • 80、Building Reliable Data Lakes at Scale with Delta Lake
  • 81、Dynamic Partition Pruning in Apache Spark
  • 82、Using Apache Spark to Solve Sessionization Problem in Batch and Streaming
  • 83、Predicting Banking Customer Needs with an Agile Approach to Analytics in the Cloud
  • 84、Powering Custom Apps at Facebook using Spark Script Transformation
  • 85、Koalas: Pandas on Apache Spark
  • 86、AI Powered Streaming Analytics for Real Time Customer Experience
  • 87、Apache Spark At Scale in the Cloud
  • 88、A Spark Based Intelligent Assistant: Making Data Exploration in Natural Language Real
  • 89、Apache Spark Core – Practical Optimization
  • 90、Briefing on the Modern ML Stack with R
  • 91、Managing the Complete Machine Learning Lifecycle with MLflow 2
  • 92、Data Democratization at Nubank
  • 93、From HelloWorld to Configurable and Reusable Apache Spark Applications in Scala
  • 94、High Performance Advanced Analytics with Spark Alchemy
  • 95、The Internals of Stateful Stream Processing in Spark Structured Streaming
  • 96、Powering Asurion’s Connected Home Platform with Spark Structured Streaming, Delta Lake, and MLflow
  • 97、Application and Challenges of Streaming Analytics and Machine Learning on Multi Variate Time Series
  • 98、Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets
  • 99、Simplify and Scale Data Engineering Pipelines with Delta Lake
  • 100、Successful AI:ML Projects with End to End Cloud Data Engineering
  • 101、Accelerating Apache Spark by Several Orders of Magnitude with GPUs
  • 102、Optimizing Delta Parquet Data Lakes for Apache Spark
  • 103、MLflow and Azure Machine Learning—The Power Couple for ML Lifecycle Management
  • 104、Building an AI Powered Retail Experience with Delta Lake, Spark, and Databricks
  • 105、Listening at the Cocktail Party with Deep Neural Networks and TensorFlow
  • 106、Stream, Stream, Stream Different Streaming Methods with Apache Spark and Kafka
  • 107、Drug Discovery and Development Using AI
  • 108、Applied Machine Learning for Ranking Products in an Ecommerce Setting
  • 109、Continuous Evaluation of Deployed Models in Production
  • 110、Scaling Data Analytics Workloads on Databricks
  • 111、Real Time Fraud Detection at Scale—Integrating Real Time Deep Link Graph Analytics with Spark AI
  • 112、Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
  • 113、How Data is Transforming the Dutch Media Industry
  • 114、Improving the Life of Data Scientists Automating ML Lifecycle through MLflow
  • 115、CyberMLToolkit Anomaly Detection as a Scalable Generic Service Over Apache Spark
  • 116、On Prem Solution for the Selection of Wind Energy Models
  • 117、Revolutionizing the Legal Industry with Spark, NLP and Azure Databricks
  • 118、Build and Deploy a Managed Machine Learning Project in 10 minutes
  • 119、Using Production Profiles to Guide Optimizations
  • 120、Power Your Delta Lake with Streaming Transactional Changes
  • 121、Unified Approach to Interpret Machine Learning Model SHAP + LIME
  • 122、AI on Spark for Malware Analysis and Anomalous Threat Detection
  • 123、Detecting Financial Fraud at Scale with Machine Learning
  • 124、Bridging the Gap Between Data Scientists and Software Engineers – Deploying Legacy Python Algorithms
  • 125、Apache Spark for Cyber Security in an Enterprise Company
  • 126、Augmenting Machine Learning with Databricks Labs AutoML Toolkit
  • 127、Machine Learning at Scale with MLflow and Apache Spark
  • 128、Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks
  • 129、Working with Complex Types in DataFrames Optics to the Rescue
  • 130、Enabling Biobank Scale Genomic Processing with Spark SQL
  • 131、Reliable Performance at Scale with Apache Spark on Kubernetes
  • 132、Unlock Value of Disparate and Complex Data Powered by Azure Databricks
  • 133、Industrializing Machine Learning on an Enterprise Azure Platform with DataBricks Experiences
  • 134、Automated Production Ready ML at Scale
  • 135、Seamless End to End Production Machine Learning with Seldon and MLflow
  • 136、Democratizing Machine Learning: Perspective from a scikit learn Creator
  • 137、Imaging the Unseen- Taking the First Picture of a Black Hole
  • 138、Scalable AI for Good
  • 139、Reinventing Payments at HSBC with a Unified Platform for Data and AI in the Cloud
  • 140、Forecasting ‘What if’ Scenarios in Retail Using ML Powered Interactive Tools
  • 141、Simplifying Model Management with MLflow
  • 142、Unified Data Analytics- Helping Data Teams Solve the World’s Toughest Problems
  • 143、Saving Energy in Homes with a Unified Approach to Data and AI
  • 144、AlphaStar- Mastering the Real Time Strategy Game StarCraft II
  • 145、New Developments in the Open Source Ecosystem: Apache Spark 3 0, Delta Lake, and Koalas
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