Spark+AI Summit 2019 PPT 下载[共124个]

为期三天的 SPARK + AI SUMMIT 2019 于 2019年04月23日-25日在旧金山(San Francisco)进行。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。作为大数据领域的顶级会议,Spark+AI Summit 2019 吸引了全球大量技术大咖参会,而且 Spark+AI Summit 越做越大,本次会议议题快接近200多个。会议的全部日程请参见:https://databricks.com/sparkaisummit/north-america/schedule

Spark+AI Summit 2019

本次会议的议题范围和 Spark+AI Summit Europe 2018 大致相同,具体如下如下:

  • Apache Spark 接下来的发展方向
  • 机器学习的最佳实践
  • 使用 MLflow 管理机器学习生命周期
  • 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


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下面议题提供 PPT 下载

  • Accelerate Your Apache Spark with Intel Optane DC Persistent Memory
  • Building Robust Production Data Pipelines with Databricks Delta
  • Cooperative Task Execution for Apache Spark
  • Deploying Enterprise Scale Deep Learning in Actuarial Modeling at Nationwide
  • ETL Made Easy with Azure Data Factory and Azure Databricks
  • Great Models with Great Privacy: Optimizing ML and AI Over Sensitive Data
  • Horizon: Deep Reinforcement Learning at Scale
  • Improving Apache Spark’s Reliability with DataSourceV2
  • Lessons Learned Using Apache Spark for Self-Service Data Prep in SaaS World
  • Predicting Influence and Communities Using Graph Algorithms
  • Supporting Over a Thousand Custom Hive User Defined Functions
  • The Pursuit of Happiness: Building a Scalable Pipeline Using Apache Spark and NLP to Measure Customer Service Quality
  • Using Spark Mllib Models in a Production Training and Serving Platform: Experiences and Extensions
  • A Journey to Building an Autonomous Streaming Data Platform—Scaling to Trillion Events Monthly at Nvidia
  • Apache Spark on K8S Best Practice and Performance in the Cloud
  • Building Robust Production Data Pipelines with Databricks Delta
  • Elastify Cloud-Native Spark Application with Persistent Memory
  • Geospatial Analytics at Scale with Deep Learning and Apache Spark
  • Large-Scale Malicious Domain Detection with Spark AI
  • Migrating to Apache Spark at Netflix
  • Smart Join Algorithms for Fighting Skew at Scale
  • The More the Merrier: Scaling Model Building Infrastructure at Zendesk
  • Apache Arrow-Based Unified Data Sharing and Transferring Format Among CPU and Accelerators
  • Apache Spark and Sights at Speed: Streaming, Feature Management, and Execution
  • Data-Driven Transformation: Leveraging Big Data at Showtime with Apache Spark
  • Lifecycle Inference on Unreliable Event Data
  • Moving a Fraud-Fighting Random Forest from scikit-learn to Spark with MLlib, MLflow, and Jupyter
  • In-Memory Evolution in Apache Spark
  • Productizing Structured Streaming Jobs
  • Scaling Apache Spark at Facebook
  • Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Impact
  • SparkML: Easy ML Productization for Real-Time Bidding
  • Threat Detection in Surveillance Videos
  • Vectorized Query Execution in Apache Spark at Facebook
  • A Distributed Deep Learning Approach for the Mitosis Detection from Big Medical Images
  • Apache Spark Data Governance Best Practices—Lessons Learned from Centers for Medicare and Medicaid Services
  • Best Practices for Hyperparameter Tuning with MLflow
  • Building an Enterprise Data Platform with Azure Databricks to Enable Machine Learning and Data Science at Scale at Sam’s Club
  • Interpretable AI: Not Just For Regulators
  • Making Nested Columns as First Citizen in Apache Spark SQL
  • Neo4j Morpheus: Interweaving Table and Graph Data with SQL and Cypher in Apache Spark
  • Redis + Structured Streaming—A Perfect Combination to Scale-Out Your Continuous Applications
  • Scaling Apache Spark on Kubernetes at Lyft
  • Simplifying Change Data Capture using Databricks Delta
  • A Virtual Assistant Ecosystem for Workflow and Workplace Optimization
  • A “Real-Time” Architecture for Machine Learning Execution with MLeap
  • Accelerating Machine Learning on Databricks Runtime
  • Accelerating Spark MLlib and DataFrame with Vector Processor “SX-Aurora TSUBASA”

  • DevOps for Applications in Azure Databricks: Creating Continuous Integration Pipelines on Azure Using Azure Databricks and Azure DevOps
  • Explain Yourself: Why You Get the Recommendations You Do
  • Managing Apache Spark Workload and Automatic Optimizing
  • Optimizing Delta/Parquet Data Lakes for Apache Spark
  • Use Machine Learning to Get the Most out of Your Big Data Clusters
  • Using S3 Select to Deliver 100X Performance Improvements Versus the Public Cloud
  • Improve ML Predictions using Connected Feature Extraction
  • Cosco: An Efficient Facebook-Scale Shuffle Service
  • Creating an Omni-Channel Customer Experience with ML, Apache Spark, and Azure Databricks
  • How Graph Technology is Changing AI
  • Lessons in Linear Algebra at Scale with Apache Spark : Let’s Make the Sparse Details a Bit More Dense
  • Optimizing Performance and Computing Resource Efficiency of In-Memory Big Data Analytics with Disaggregated Persistent Memory
  • Running R at Scale with Apache Arrow on Spark
  • Self-Service Apache Spark Structured Streaming Applications and Analytics
  • Writing Continuous Applications with Structured Streaming PySpark API
  • Building Resilient and Scalable Data Pipelines by Decoupling Compute and Storage
  • Accelerating Genomics SNPs Processing and Interpretation with Apache Spark
  • Analyzing 2TB of Raw Trace Data from a Manufacturing Process: A First Use Case of Apache Spark for Semiconductor Wafers from Real Industry
  • Apache Spark NLP: Extending Spark ML to Deliver Fast, Scalable, and Unified Natural Language Processing
  • High Performance Transfer Learning for Classifying Intent of Sales Engagement Emails: An Experimental Study
  • Leveraging NLP and Deep Learning for Document Recommendations in the Cloud
  • Real-Time Analytics and Actions Across Large Data Sets with Apache Spark
  • Reimagining Devon Energy’s Data Estate with a Unified Approach to Integrations, Analytics, and Machine Learning
  • Tackling Network Bottlenecks with Hardware Accelerations: Cloud vs. On-Premise
  • The Rule of 10,000 Spark Jobs: Learning From Exceptions and Serializing Your Knowledge
  • Data Prep for Data Science in Minutes—A Real World Use Case Study of Telematics
  • A Deep Dive into Query Execution Engine of Spark SQL
  • Apache Spark at Airbnb
  • Assessing Drug Safety Using AI
  • Balancing Automation and Explanation in Machine Learning
  • Bridging the Gap Between Datasets and DataFrames
  • From Genomics to Medicine: Advancing Healthcare at Scale
  • Headaches and Breakthroughs in Building Continuous Applications
  • How McAfee Built High-Quality Pipelines with Azure Databricks to Power Customer Insights on 250TB+ of Data: Lessons Learned in Data Governance and Lineage
  • Make your PySpark Data Fly with Arrow!

  • TensorFlow Extended: An End-to-End Machine Learning Platform for TensorFlow
  • An AI-Powered Chatbot to Simplify Apache Spark Performance Management
  • Apache Spark Data Validation
  • Continuous Applications at Scale of 100 Teams with Databricks Delta and Structured Streaming
  • Data Agility—A Journey to Advanced Analytics and Machine Learning at Scale
  • How Australia’s National Health Services Directory Improved Data Quality, Reliability, and Integrity with Databricks Delta and Structured Streaming
  • Infrastructure for Deep Learning in Apache Spark
  • Near Real-Time Analytics with Apache Spark: Ingestion, ETL, and Interactive Queries
  • Scaling Ride-Hailing with Machine Learning on MLflow
  • Simplify Distributed TensorFlow Training for Fast Image Categorization at Starbucks
  • Tangram: Distributed Scheduling Framework for Apache Spark at Facebook
  • Accelerating Machine Learning Workloads and Apache Spark Applications via CUDA and NCCL
  • Apache Spark Serving: Unifying Batch, Streaming, and RESTful Serving
  • Automating Predictive Modeling at Zynga with PySpark and Pandas UDFs
  • Cobrix: A Mainframe Data Source for Spark SQL and Streaming
  • Databricks: What We Have Learned by Eating Our Dog Food
  • Designing Structured Streaming Pipelines—How to Architect Things Right
  • How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform
  • Journey to Creating a 360 View of the Customer: Implementing Big Data Strategies with a Data Lake and Databricks
  • Parallelizing with Apache Spark in Unexpected Ways
  • Scaling ML-Based Threat Detection For Production Cyber Attacks
  • Updates from Project Hydrogen: Unifying State-of-the-Art AI and Big Data in Apache Spark
  • Working with 1 Million Time Series a Day: How to Scale Up a Predictive Analytics Model Switching from Sequential to Parallel Computing
  • Advanced Hyperparameter Optimization for Deep Learning with MLflow
  • Fast and Reliable Apache Spark SQL Engine
  • Introducing .NET Bindings for Apache Spark
  • Life is but a Stream
  • Monitoring of GPU Usage with Tensorflow Models Using Prometheus
  • The Azure Cognitive Services on Spark: Clusters with Embedded Intelligent Services
  • Understanding Query Plans and Spark UIs
  • Using Deep Learning on Apache Spark to Diagnose Thoracic Pathology from Chest X-rays
  • ROCm and Distributed Deep Learning on Spark and TensorFlow
  • SparkWeaver: Full-Stack Solution to Accelerate Real-Time DNN Applications on FPGA-Enabled Spark Streaming
  • Unifying Streaming and Historical Telemetry Data For Real-time Performance Reporting
  • Cloud Experience: Data-driven Applications Made Simple and Fast
  • Connecting the Dots: Integrating Apache Spark into Production Pipelines
  • DASK and Apache Spark
  • Databricks + Snowflake: Catalyzing Data and AI Initiatives
  • How to Extend Apache Spark with Customized Optimizations
  • Massive-Scale Entity Resolution Using the Power of Apache Spark and Graph
  • Modular Apache Spark: Transform Your Code in Pieces
  • Apache Spark Listeners: A Crash Course in Fast, Easy Monitoring
  • Applications of Deep Learning in Telematics
  • Building Sessionization Pipeline at Scale with Databricks Delta
  • Cloud Storage Spring Cleaning: A Treasure Hunt
  • Deep Dive of ADBMS Migration to Apache Spark—Use Cases Sharing
  • Distributed ML/DL with Ignite ML Module Using Apache Spark as Database
  • Splice Machine’s use of Apache Spark and MLflow
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