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。
本次会议的议题范围具体如下:
- 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。
下载途径
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。
百度网盘下载:
视频下载(共135个) => 链接: https://pan.baidu.com/s/1WtbPmfn2dOfXWVSluSWRAA,具体参见 Spark+AI Summit Europe 2019 高清视频下载[共135个]
全部 PPT => 链接: 链接:https://pan.baidu.com/s/1Obmh7fb5YaQV0u8R_kURTQ
密码请关注微信公众号 iteblog_hadoop 回复 8424 获取。
全部可下载的PPT
由于这次会议的 PPT 还在更新,截止到目前只可以下载98个,剩余的 PPT 我将这这篇文章进行更新,敬请关注。
- A Recommender Story - Improving Backend Data Quality While Reducing Costs
- A Spark-Based Intelligent Assistant - Making Data Exploration in Natural Language Real
- ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scale Storage and Analytics
- AI-Powered Streaming Analytics for Real-Time Customer Experience
- Accelerating Apache Spark with Intel QuickAssist Technology
- Accelerating Astronomical Discoveries with Apache Spark
- Accelerating Real Time Video Analytics on a Heterogenous CPU + FPGA Platform
- Apache Spark AI Use Case in Telco - Network Quality Analysis and Prediction with Geospatial Visualization
- Apache Spark At Scale in the Cloud
- Apache Spark Core – Practical Optimization
- Apache Spark Side of Funnels
- Apache Spark's Built-in File Sources in Depth
- Application and Challenges of Streaming Analytics and Machine Learning on Multi-Variate Time Series Data for Smart Manufacturing
- Applications of Time Travel with Delta Lake
- Assessing Graph Solutions for Apache Spark
- Astronomical Data Processing on the LSST Scale with Apache Spark
- Asynchronous Hyperparameter Optimization with Apache Spark
- Augmenting Machine Learning with Databricks Labs AutoML Toolkit
- Automating Loss Prevention Using NLP with FastAI on Azure Databricks
- Best Practices for Building and Deploying Data Pipelines in Apache Spark
- Blue Pill-Red Pill - The Matrix of Thousands of Data Streams
- Briefing on the Modern ML Stack with R
- Building A Feature Factory
- Building Data Intensive Analytic Application on Top of Delta Lakes
- Building Reliable Data Lakes at Scale with Delta Lake
- Building a Knowledge Graph with Spark and NLP - How We Recommend Novel Drugs to our Scientists
- Building a Modern FinTech Big Data Infrastructure
- Commercial Analytics at Scale in Pharma - From Hackathon to MVP with Azure Databricks
- Continuous Deployment for Deep Learning
- Continuous Evaluation of Deployed Models in Production
- Cosmos DB Real-time Advanced Analytics Workshop
- CyberMLToolkit - Anomaly Detection as a Scalable Generic Service Over Apache Spark
- Data Warehousing with Spark Streaming at Zalando
- Databricks Delta Lake and Its Benefits
- Deep Anomaly Detection from Research to Production Leveraging Spark and Tensorflow
- Deep Learning Pipelines for High Energy Physics using Apache Spark with Distributed Keras on Analytics Zoo
- Deep Learning with DL4J on Apache Spark - Yeah it’s Cool, but are You Doing it the Right Way
- Deploying End-to-End Deep Learning Pipelines with ONNX
- Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
- Downscaling - The Achilles heel of Autoscaling Apache Spark Clusters
- Driver Location Intelligence at Scale using Apache Spark, Delta Lake, and MLflow on Databricks
- Dynamic Partition Pruning in Apache Spark
- Enabling Biobank-Scale Genomic Processing with Spark SQL
- Encrypted Computation in Apache Spark
- End-to-End Spark-TensorFlow-PyTorch Pipelines with Databricks Delta
- Extending Spark Graph for the Enterprise with Morpheus and Neo4j
- Extending Spark SQL 2.4 with New Data Sources (Live Coding Session)
- From HelloWorld to Configurable and Reusable Apache Spark Applications in Scala
- Getting Started Contributing to Apache Spark – From PR, CR, JIRA, and Beyond
- High-Performance Advanced Analytics with Spark-Alchemy
- How to Automate Performance Tuning for Apache Spark
- Improving Apache Spark Downscaling
- Improving Apache Spark by Taking Advantage of Disaggregated Architecture
- Internals of Speeding up PySpark with Arrow
- Introduction to TensorFlow 2.0
- Koalas - Making an Easy Transition from Pandas to Apache Spark
- Koalas - Pandas on Apache Spark
- Lessons Learned Replatforming A Large Machine Learning Application To Apache Spark
- Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonomous Driving
- Listening at the Cocktail Party with Deep Neural Networks and TensorFlow
- MLflow and Azure Machine Learning—The Power Couple for ML Lifecycle Management
- Making Homes Efficient and Comfortable Using AI and IoT Data
- Maps and Meaning- Graph-based Entity Resolution in Apache Spark & GraphX
- Migrating Apache Spark ML Jobs to Spark + Tensorflow on Kubeflow
- Modern ETL Pipelines with Change Data Capture
- Near Real-Time Data Warehousing with Apache Spark and Delta Lake
- No REST till Production – Building and Deploying 9 Models to Production in 3 weeks
- On-Prem Solution for the Selection of Wind Energy Models
- Optimizing Delta - Parquet Data Lakes for Apache Spark
- Performance Analysis of Apache Spark and Presto in Cloud Environments
- Performance Troubleshooting Using Apache Spark Metrics
- Physical Plans in Spark SQL
- Power Your Delta Lake with Streaming Transactional Changes
- Powering Custom Apps at Facebook using Spark Script Transformation
- Predicting Banking Customer Needs with an Agile Approach to Analytics in the Cloud
- Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Analytics with Spark AI
- Retrieving Visually-Similar Products for Shopping Recommendations using Spark and Tensorflow
- Scalable Time Series Forecasting and Monitoring using Apache Spark and ElasticSearch
- Seamless End-to-End Production Machine Learning with Seldon and MLflow
- Simplify and Scale Data Engineering Pipelines with Delta Lake
- Solving sessionization problem with Apache Spark batch and streaming processing
- Spark Operator—Deploy, Manage and Monitor Spark clusters on Kubernetes
- Spark SQL Bucketing at Facebook
- Stream processing - choosing the right tool for the job
- Streaming Analytics for Financial Enterprises
- Successful AI:ML Projects with End-to-End Cloud Data Engineering
- Tactical Data Science Tips - Python and Spark Together
- The Internals of Stateful Stream Processing in Spark Structured Streaming
- The Parquet Format and Performance Optimization Opportunities
- Transforming AI with Graphs - Real World Examples using Spark and Neo4j
- Unified Approach to Interpret Machine Learning Model - SHAP + LIME
- Updates from Project Hydrogen - Unifying State-of-the-Art AI and Big Data in Apache Spark
- Using Production Profiles to Guide Optimizations
- Using PySpark to Scale Markov Decision Problems for Policy Exploration
- Vectorized R Execution in Apache Spark
- Working with Complex Types in DataFrames - Optics to the Rescue
- Zipline—Airbnb’s Declarative Feature Engineering Framework
- Data Democratization
- AI on Spark for Malware Analysis and Anomalous Threat Detection
- Accelerating Apache Spark by Several Orders of Magnitude with GPUs and RAPIDS Library
- Apache Spark for Cyber Security in an Enterprise Company
- Applied Machine Learning for Ranking Products in an Ecommerce Setting
- Auto-Pilot for Apache Spark Using Machine Learning
- Automated Production Ready ML at Scale
- Bridging the Gap Between Data Scientists and Software Engineers – Deploying Legacy Python Algorithms to Apache Spark with Minimum Pain
- Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets
- Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks
- Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks
- Detecting Financial Fraud at Scale with Machine Learning
- Drug Discovery and Development Using AI
- Graph Features in Spark 3.0 - Integrating Graph Querying and Algorithms in Spark Graph
- How Data is Transforming the Dutch Media Industry
- Improving the Life of Data Scientists - Automating ML Lifecycle through MLflow
- Industrializing Machine Learning on an Enterprise Azure Platform with DataBricks - Experiences and Feedbacks
- Machine Learning at Scale with MLflow and Apache Spark
- Managing the Complete Machine Learning Lifecycle with MLflow
- Petabytes, Exabytes, and Beyond - Managing Delta Lakes for Interactive Queries at Scale
- Powering Asurion's Connected Home Platform with Spark Structured Streaming, Delta Lake, and MLflow
- Reliable Performance at Scale with Apache Spark on Kubernetes
- Revolutionizing the Legal Industry with Spark, NLP and Azure Databricks
- Scaling Data Analytics Workloads on Databricks
- Stream, Stream, Stream - Different Streaming Methods with Apache Spark and Kafka
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