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。

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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

下载途径

CSDN 下载：https://download.csdn.net/download/w397090770/11799138，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 2431 获取。

百度网盘下载：
视频下载 => 链接:https://pan.baidu.com/s/1dvV8YHNaDMCNIvJHJuOETw
全部 PPT => 链接:https://pan.baidu.com/s/1y8XuWoUFKcp2x6RWJy1FTg
密码请关注微信公众号 iteblog_hadoop 回复 2431 获取。

全部可下载的PPT

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
• 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
• 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