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