Data + AI Summit 2021 全部超清 PPT 下载

Posted 2021-06-29 过往记忆

Data + AI Summit 2021 于2021年05月24日至28日举行。本次会议是在线举办的，一共为期五天，第一、二天是培训，第三天到第五天是正式会议。本次会议有超过200个议题，演讲嘉宾包括业界、研究和学术界的专家，会议涵盖来自从业者的技术内容，他们将使用 Apache Spark™、Delta Lake、MLflow、Structured Streaming、BI和SQL分析、深度学习和机器学习框架来解决棘手的数据问题。会议的全部日程请参见：https://databricks.com/dataaisummit/north-america-2021/sessions

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按照惯例，这次会议的 KeyNote 部分数砖发布了一些新产品，比如 Delta Sharing、Delta Live Tables、Unity Catalog 等等。本次会议有些干货大家可以看下的。在接下来的几天，本公众号也会对一些比较有意思的议题进行介绍，敬请关注本公众号。

本次会议的议题范围具体如下：

•Apache Spark™, Delta Lake, MLflow, PyTorch, TensorFlow, Transformers 等最佳实践和用户案例；•数据工程，包括流架构•使用数据仓库（data warehouse）和数据湖（data lakes）进行 SQL 分析和 BI；•数据科学，包括 Python 生态系统；•机器学习和深度学习应用

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可下载的PPT

下面议题提供 PPT 下载，共186个。

•10 Things Learned Releasing Databricks Enterprise Wide•5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop•A Collaborative Data Science Development Workflow•A Fast Decision Rule Engine for Anomaly Detection•A High Performance Mutable Engagement Activity Delta Lake•A Practical Enterprise Feature Store on Delta Lake•Accelerate Data Science Initiatives: Databricks & Privacera•Accelerate Your ML Pipeline with AutoML and MLflow•Accelerating Data Ingestion with Databricks Autoloader•Advanced Model Comparison and Automated Deployment Using ML•Advanced Natural Language Processing with Apache Spark NLP•Advanced SQL For Data Scientists•AI Data Acquisition and Governance: Considerations for Success•AI Modernization at AT&T and the Application to Fraud with Databricks•AI-Driven Personalized Email Marketing•Analytics-Enabled Experiences: The New Secret Weapon•Anomaly Detection at Scale!•Architecting Agile Data Applications for Scale•Architect’s Open-Source Guide for a Data Mesh Architecture•Automated Background Removal Using PyTorch•Automated Metadata Management in Data Lake – A CI/CD Driven Approach•Automatic ICD-10 Code Assignment to Consultations•Automating Data Quality Processes at Reckitt•Auto-Train a Time-Series Forecast Model With AML + ADB•Best Practices for Enabling Speculative Execution on Large Scale Platforms•Bootstrapping of PySpark Models for Factorial A/B Tests•BOTS TESTING BOTS: From manual to automated testing for conversational AI•Bridging the Completeness of Big Data on Databricks•Bring Your Own Container: Using Docker Images In Production•Brokering Data: Accelerating Data Evaluation with Databricks White Label•Build Large-Scale Data Analytics and AI Pipeline Using RayDP•Build Real-Time Applications with Databricks Streaming•Building a Data Science as a Service Platform in Azure with Databricks•Building A Product Assortment Recommendation Engine•Building an ML Platform with Ray and MLflow•Building Data Quality pipelines with Apache Spark and Delta Lake•Building Data Science into Organizations: Field Experience•Building End-to-End Delta Pipelines on GCP•Building Lakehouses on Delta Lake with SQL Analytics Primer•Building Source of Truth Place Data at Scale•Building the Artificially Intelligent Enterprise•Building the Foundations of an Intelligent, Event-Driven Data Platform at EFSA•Scaling AI At H&M•Catch Me If You Can: Keeping Up With ML Models in Production•ChakraView – A 360° Approach to Data Quality•Change Data Feed in Delta•Choose Your Weapon: Comparing Spark on FPGAs vs GPUs•CI/CD in MLOps – Implementing a Framework for Self-Service Everything•Code Once Use Often with Declarative Data Pipelines•Commercializing Alternative Data•Comprehensive View on Intervals in Apache Spark 3.2•Configuration Driven Reporting On Large Dataset Using Apache Spark•Considerations for Data Access in the Lakehouse•Consolidating MLOps at One of Europe’s Biggest Airports•Conversational AI with Transformer Models•Creating an 86,000 Hour Speech Dataset with Apache Spark and TPUs•Creating Reusable Geospatial Pipelines•Credit Card Fraud Detection Using ML In Databricks•Customer Experience at Disney+ Through Data Perspective•Data Discovery at Databricks with Amundsen•Data Distribution and Ordering for Efficient Data Source V2•Data Quality With or Without Apache Spark and Its Ecosystem•Data Security at Scale through Spark and Parquet Encryption•Databricks: A Tool That Empowers You To Do More With Data•Deep Dive into the New Features of Apache Spark 3.1•Degrading Performance? You Might be Suffering From the Small Files Syndrome•Delight: An Improved Apache Spark UI, Free, and Cross-Platform•Delivering Insights from 20M+ Smart Homes with 500M+ Devices•Delta Lake Streaming: Under the Hood•Democratizing Data Quality Through a Centralized Platform•Detecting Anomalous Behavior with Surveillance Analytics•DevOps for Databricks•Drifting Away: Testing ML Models in Production•Drug and Vaccine Discovery: Knowledge Graph + Apache Spark•Drug Repurposing using Deep Learning on Knowledge Graphs•Effective AIOps with Open Source Software in a Week•Efficient Distributed Hyperparameter Tuning with Apache Spark•Efficient Large-Scale Language Model Training on GPU Clusters•Empower Splunk and other SIEMs with the Databricks Lakehouse for Cybersecurity•Empowering Real Time Patient Care Through Spark Streaming•Empowering Zillow’s Developers with Self-Service ETL•Entity Resolution Using Patient Records at CMMI•Experimentation to Industrialization: Implementing MLOps•Extending Machine Learning Algorithms with PySpark•FlorenceAI: Reinventing Data Science at Humana•From Chatbots to Augmented Conversational Assistants•From Vaccine Management to ICU Planning: How CRISP Unlocked the Power of Data During a Pandemic•FrugalML: Using ML APIs More Accurately and Cheaply•Fully Utilizing Spark for Data Validation•Funnel Analysis with Apache Spark and Druid•Gain 3 Benefits with Delta Sharing•Gender Prediction with Databricks AutoML Pipeline•Getting Started with Databricks SQL Analytics•Giving Away The Keys To The Kingdom: Using Terraform To Automate Databricks•Graph-Powered Machine Learning•Growing the Delta Ecosystem to Rust and Python with Delta-RS•How Adobe uses Structured Streaming at Scale•How Machine Learning and AI Can Support the Fight Against COVID-19•How to Build a ML Platform Efficiently Using Open-Source•How to use Apache TVM to optimize your ML models•How We Optimize Spark SQL Jobs With parallel and sync IO•How We Scaled Bert To Serve 1+ Billion Daily Requests on CPU•Hybrid Apache Spark Architecture with YARN and Kubernetes•Hyperspace for Delta Lake•Image Processing on Delta Lake•Importance of ML Reproducibility & Applications with MLfLow•Improving Apache Spark for Dynamic Allocation and Spot Instances•Improving Power Grid Reliability Using IoT Analytics•Infrastructure Agnostic Machine Learning Workload Deployment•Intro to Delta Lake•Introducing Delta Live Tables: Make Reliable ETL Easy on Delta Lake•Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue•Jeeves Grows Up: An AI Chatbot for Performance and Quality•Keeping Identity Graphs In Sync With Apache Spark•KFServing, Model Monitoring with Apache Spark and a Feature Store•Koalas: How Well Does Koalas Work?•Large Scale Geospatial Indexing and Analysis on Apache Spark•Large Scale Lakehouse Implementation Using Structured Streaming•Learn to Use Databricks for Data Science•Learn to Use Databricks for the Full ML Lifecycle•Machine Learning CI/CD for Email Attack Detection•Machine Learning with PyCaret•Magnet Shuffle Service: Push-based Shuffle at LinkedIn•Managing Millions of Tests Using Databricks•Managing R&D Data on Parallel Compute Infrastructure•Massive Data Processing in Adobe Using Delta Lake•Migrating ETL Workflow to Apache Spark at Scale in Pinterest•Migrating Your Data Platform At a High Growth Startup•Misusing MLflow To Help Deduplicate Data At Scale•MLCommons: Better ML for Everyone•MLflow Model Serving•Model Monitoring at Scale with Apache Spark and Verta•Modelling Customer Lifetime Revenue for Subscription Business•Modernizing to a Cloud Data Architecture•Modularized ETL Writing with Apache Spark•Monitor Apache Spark 3 on Kubernetes using Metrics and Plugins•Natural Language Query and Conversational Interface to Apache Spark•NLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobil•Northwestern Mutual Journey – Transform BI Space to Cloud•Object Detection with Transformers•Observability for Data Pipelines With OpenLineage•Offer Recommendation System with Apache Spark at Burger King•Optimizing the Catalyst Optimizer for Complex Plans•PandasUDFs: One Weird Trick to Scaled Ensembles•Phar Data Platform: From the Lakehouse Paradigm to the Reality•Play Head Time Analysis On OTT Video At Scale•Portable UDFs: Write Once, Run Anywhere•Predicting Optimal Parallelism for Data Analytics•Processing Large Datasets for ADAS Applications using Apache Spark•Productionalizing Machine Learning Solutions with Effective Tracking, Monitoring, and Management•Productionizing Machine Learning in Our Health and Wellness Marketplace•Productionzing ML Model Using MLflow Model Serving•Radical Speed for SQL Queries on Databricks: Photon Under the Hood•Raven: End-to-end Optimization of ML Prediction Queries•Real-world Strategies for Debugging Machine Learning Systems•Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink•Re-imagine Data Monitoring with whylogs and Spark•Role of Data Accessibility During Pandemic•RWE & Patient Analytics Leveraging Databricks – A Use Case•Sawtooth Windows for Feature Aggregations•Scaling and Modernizing Data Platform with Databricks•Scaling and Unifying SciKit Learn and Apache Spark Pipelines•Scaling AutoML-Driven Anomaly Detection With Luminaire•Scaling Online ML Predictions At DoorDash•Scaling Privacy in a Spark Ecosystem•Scaling your Data Pipelines with Apache Spark on Kubernetes•Semantic Image Logging Using Approximate Statistics & MLflow•Simplify Data Conversion from Spark to TensorFlow and PyTorch•Speed up UDFs with GPUs using the RAPIDS Accelerator•SQL Analytics Powering Telemetry Analysis at Comcast•Stage Level Scheduling Improving Big Data and AI Integration•Structured Streaming Use-Cases at Apple•Superworkflow of Graph Neural Networks with K8S and Fugue•Tensors Are All You Need: Faster Inference with Hummingbird•The Critical Missing Component in the Production ML Stack•The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix•The Rise of Vector Data•The Rise of ZStandard: Apache Spark/Parquet/ORC/Avro•Towards Personalization in Global Digital Health•Unified MLOps: Feature Stores & Model Deployment•Video Analytics At Scale: DL, CV, ML On Databricks Platform•Weekday Demand Sensing at Walmart•What’s New with Databricks Machine Learning•Why APM Is Not the Same As ML Monitoring•Wizard Driven AI Anomaly Detection with Databricks in Azure•You Can Do It in SQL