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TUESDAY, OCTOBER 15 – TRAINING

07:00

Registration

  • Registration
  • 720 MINS
09:00

Training: Apache Spark Programming

  • トレーニング
  • 180 MINS
  • Room: D201
09:00

Training: Apache Spark Tuning and Best Practices

  • トレーニング
  • 180 MINS
  • Room: Elicium 1
09:00

Training: Building Data Pipelines for Apache Spark with Databricks Delta Lake

  • トレーニング
  • 180 MINS
  • Room: E102
09:00

Training: Data Science With Apache Spark 2.x

  • トレーニング
  • 180 MINS
  • Room: Elicium 2
09:00

Training: Half-Day Prep Course + Databricks Certification Exam

  • トレーニング
  • 180 MINS
  • Room: E103
09:00

Training: Machine Learning in Production: MLflow and Model Deployment

  • トレーニング
  • 180 MINS
  • Room: D203
09:00

Training: Understand and Apply Deep Learning with Keras, TensorFlow and Apache Spark

  • トレーニング
  • 180 MINS
  • Room: E107
12:00

Lunch

  • Lunch
  • 60 MINS
13:00

Training: Apache Spark Programming

  • トレーニング
  • 240 MINS
  • Room: D201
13:00

Training: Apache Spark Tuning and Best Practices

  • トレーニング
  • 240 MINS
  • Room: Elicium 1
13:00

Training: Building Data Pipelines for Apache Spark with Databricks Delta Lake

  • トレーニング
  • 240 MINS
  • Room: E102
13:00

Training: Data Science With Apache Spark 2.x

  • トレーニング
  • 240 MINS
  • Room: Elicium 2
13:00

Training: Machine Learning in Production: MLflow and Model Deployment

  • トレーニング
  • 240 MINS
  • Room: D203
13:00

Training: Understand and Apply Deep Learning with Keras, TensorFlow and Apache Spark

  • トレーニング
  • 240 MINS
  • Room: E107
18:00

Ask Me Anything (AMA): Apache Spark Committers and Databricks Founders

  • Break
  • 20 MINS
  • Room: Expo Hall
18:30

Amsterdam Data Science Meetup – Drinks & Data: Tech Talks in Data Science, AI and Machine Learning

  • ミートアップ
  • 150 MINS
  • Room: Forum

WEDNESDAY, OCTOBER 16 – CONFERENCE

07:00

Registration

  • Registration
  • 600 MINS
09:00

AlphaStar: Mastering the Real-Time Strategy Game StarCraft II

Oriol Vinyals (Google)
  • 基調講演
  • 90 MINS
  • Room: Auditorium
09:00

New Developments in the Open Source Ecosystem: Apache Spark 3.0, Delta Lake, and Koalas

Michael Armbrust (Databricks) Brooke Wenig (Databricks) Burak Yavuz (Databricks)
  • 基調講演
  • 90 MINS
  • Room: Auditorium
09:00

Saving Energy in Homes with a Unified Approach to Data and AI – EU

Dr. Stephen Galsworthy (Quby)
  • 基調講演
  • 90 MINS
  • Room: Auditorium
09:00

Unified Data Analytics: Helping Data Teams Solve the World’s Toughest Problems

Ali Ghodsi (Databricks)
  • 基調講演
  • 90 MINS
  • Room: Auditorium
10:00

Expo Hall Open

  • ミートアップ
  • 480 MINS
  • Room: Expo Hall
10:30

Break

  • Break
  • 30 MINS
10:35

Ask Me Anything (AMA): Delta Lake

  • Break
  • 20 MINS
  • Room: Expo Hall
11:00

Accelerating Real Time Video Analytics on a Heterogenous CPU + FPGA Platform

Bhoomika Sharma (Megh Computing, Inc.)
  • AI
  • 40 MINS
  • Room: Emerald
11:00

Apache Spark At Scale in the Cloud

Rose Toomey (Bloomberg)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
11:00

Automating Loss Prevention Using NLP with FastAI on Azure Databricks: A Gentle Walk-Through with PetSmart

Mike Vedomske (PetSmart)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
11:00

Delta Lake を使用して大規模で信頼性の高いデータレイクを構築する

Andreas Neumann (Databricks) Tathagata Das (Databricks) Mukul Murthy (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: G104
11:00

Continuous Deployment for Deep Learning

Nick Pentreath (IBM)
  • データサイエンス
  • 40 MINS
  • Room: G102
11:00

Deep Learning Pipelines for High Energy Physics using Apache Spark with Distributed Keras on Analytics Zoo

Luca Canali (CERN)
  • Data & ML Research
  • 40 MINS
  • Room: E102
11:00

Extending Spark Graph for the Enterprise with Morpheus and Neo4j

Martin Junghanns (Neo4j) Sören Reichardt (Neo4j)
  • Developer
  • 40 MINS
  • Room: Elicium 2
11:00

Koalas: Making an Easy Transition from Pandas to Apache Spark

Tim Hunter (ABN AMRO) Takuya Ueshin (Databricks)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
11:00

Near Real-Time Data Warehousing with Apache Spark and Delta Lake

Jasper Groot (Eventbrite)
  • Developer
  • 40 MINS
  • Room: Auditorium
11:00

Vectorized R Execution in Apache Spark

Hyukjin Kwon (Databricks)
  • Developer
  • 40 MINS
  • Room: E103
11:50

.NET for Apache Spark

Rahul Potharaju (Microsoft) Terry Kim (Microsoft)
  • Developer
  • 40 MINS
  • Room: Elicium 2
11:50

Apache Spark’s Built-in File Sources in Depth

Gengliang Wang (Databricks)
  • Developer
  • 40 MINS
  • Room: Auditorium
11:50

Building Reliable Data Lakes at Scale with Delta Lake – cont

Andreas Neumann (Databricks) Tathagata Das (Databricks) Mukul Murthy (Databricks)
  • チュートリアル
  • 40 MINS
  • Room: G104
11:50

Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Databricks

Peter Webb (GlaxoSmithKline)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
11:50

Data Reproducibility, Audits, Immediate Rollbacks, and Other Applications of Time Travel with Delta Lake

Kyle Weller (Microsoft)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
11:50

Improving Apache Spark by Taking Advantage of Disaggregated Architecture

Chenzhao Guo (Intel) Carson Wang (Intel)
  • Data & ML Research
  • 40 MINS
  • Room: E102
11:50

Internals of Speeding up PySpark with Arrow

Ruben Berenguel (Hybrid Theory)
  • Developer
  • 40 MINS
  • Room: E103
11:50

Transforming AI with Graphs: Real World Examples using Spark and Neo4j

Alicia Frame (Neo4j)
  • データサイエンス
  • 40 MINS
  • Room: G102
11:50

Using PySpark to Scale Markov Decision Problems for Policy Exploration

Justin Brandenburg (Databricks)
  • AI
  • 40 MINS
  • Room: Emerald
11:50

Zipline—Airbnb’s Declarative Feature Engineering Framework

Varant Zanoyan (Airbnb) Evgeny Shapiro (Airbnb)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
12:30

Lunch

  • Lunch
  • 60 MINS
13:40

Asynchronous Hyperparameter Optimization with Apache Spark

Jim Dowling (Logical Clocks AB) Moritz Meister (Logical Clocks AB)
  • Data & ML Research
  • 40 MINS
  • Room: E102
13:40

Building Data Intensive Analytic Application on Top of Delta Lakes

Ganesh Chand (Databricks) Ravi Gawai (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
13:40

Deep Learning with DL4J on Apache Spark: Yeah it’s Cool, but are You Doing it the Right Way?

Guglielmo Iozzia (MSD)
  • データサイエンス
  • 40 MINS
  • Room: G102
13:40

Downscaling: The Achilles heel of Autoscaling Apache Spark Clusters

Prakhar Jain (Qubole) Venkata Krishnan Sowrirajan (LinkedIn)
  • Developer
  • 40 MINS
  • Room: Elicium 2
13:40

Extending Spark SQL 2.4 with New Data Sources (Live Coding Session)

Jacek Laskowski (Development and training services)
  • Developer
  • 40 MINS
  • Room: Auditorium
13:40

How to Automate Performance Tuning for Apache Spark

Jean-Yves Stephan (Data Mechanics) Julien Dumazert (Data Mechanics)
  • チュートリアル
  • 40 MINS
  • Room: G104
13:40

Making Homes Efficient and Comfortable Using AI and IoT Data

Ellissa Verseput (Quby)
  • AI
  • 40 MINS
  • Room: Emerald
13:40

Migrating Apache Spark ML Jobs to Spark + Tensorflow on Kubeflow

Holden Karau (Apple)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
13:40

Physical Plans in Spark SQL

David Vrba (Socialbakers a.s.)
  • Developer
  • 40 MINS
  • Room: E103
13:40

Retrieving Visually-Similar Products for Shopping Recommendations using Spark and Tensorflow

Zhichao Zhong (Wehkamp)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
14:30

Allies and Adversaries : Explaining Model Reasoning via Contrasting Proximal Prototypes

Deepak Pai (Adobe, Inc.) Vijay Srivastava (Adobe, Inc.)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
14:30

Apache Spark AI Use Case in Telco: Network Quality Analysis and Prediction with Geospatial Visualization

Hongchan Roh (SK Telecom) Dooyoung Hwang (SK Telecom)
  • AI
  • 40 MINS
  • Room: Emerald
14:30

Extending Spark SQL 2.4 with New Data Sources (Live Coding Session)—continues

Jacek Laskowski (Development and training services)
  • Developer
  • 40 MINS
  • Room: Auditorium
14:30

Koalas: Pandas on Apache Spark EU

Tim Hunter (ABN AMRO) Brooke Wenig (Databricks) Niall Turbitt (Databricks)
  • チュートリアル
  • 40 MINS
  • Room: G104
14:30

Lessons Learned Replatforming A Large Machine Learning Application To Apache Spark

Taylor Hess (Morningstar Inc) Patrick Caldon (Morningstar Inc)
  • データサイエンス
  • 40 MINS
  • Room: G102
14:30

Physical Plans in Spark SQL—continues

David Vrba (Socialbakers a.s.)
  • Developer
  • 40 MINS
  • Room: E103
14:30

Predicting Banking Customer Needs with an Agile Approach to Analytics in the Cloud

Jakub Masek (MONETA Money Bank) Milan Berka (DataSentics a.s.)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
14:30

Stream Processing: Choosing the Right Tool for the Job

Giselle van Dongen (Klarrio)
  • Data & ML Research
  • 40 MINS
  • Room: E102
14:30

Streaming Analytics for Financial Enterprises

Bas Geerdink (Rabobank)
  • Developer
  • 40 MINS
  • Room: Elicium 2
14:30

Using Apache Spark to Solve Sessionization Problem in Batch and Streaming

Bartosz Konieczny (Octo Technology)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
15:20

A Spark-Based Intelligent Assistant: Making Data Exploration in Natural Language Real

Georgia Koutrika (ATHENA RESEARCH CENTER)
  • AI
  • 40 MINS
  • Room: Emerald
15:20

Assessing Graph Solutions for Apache Spark

Songting Chen (TigerGraph)
  • Developer
  • 40 MINS
  • Room: Elicium 2
15:20

Briefing on the Modern ML Stack with R

Javier Luraschi (RStudio)
  • データサイエンス
  • 40 MINS
  • Room: G102
15:20

Building a Knowledge Graph with Spark and NLP: How We Recommend Novel Drugs to our Scientists

Eliseo Papa (AstraZeneca)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
15:20

Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Architect Things Right

Tathagata Das (Databricks)
  • Developer
  • 40 MINS
  • Room: E103
15:20

Dynamic Partition Pruning in Apache Spark

Bogdan Ghit (Databricks) Juliusz Sompolski (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
15:20

Encrypted Computation in Apache Spark

Kim Laine (Microsoft)
  • Data & ML Research
  • 40 MINS
  • Room: E102
15:20

Getting Started Contributing to Apache Spark – From PR, CR, JIRA, and Beyond SAIS EU

Holden Karau (Apple)
  • Developer
  • 40 MINS
  • Room: Auditorium
15:20

Koalas: Pandas on Apache Spark (continued)

Tim Hunter (ABN AMRO) Brooke Wenig (Databricks) Niall Turbitt (Databricks)
  • チュートリアル
  • 40 MINS
  • Room: G104
15:20

No REST till Production – Building and Deploying 9 Models to Production in 3 weeks

Charmee Patel (Syntasa)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
16:00

Ask Me Anything (AMA): Koalas

  • Break
  • 20 MINS
  • Room: Expo Hall
16:00

Break #2

  • Break
  • 30 MINS
16:00

Financial Services Networking Event

Matthijs van Dorth (ABN AMRO) Thimo ten Veen (ABN AMRO) Jonathan Chin (ARM Insight)
  • ミートアップ
  • 180 MINS
  • Room: D202
16:30

Accelerating Astronomical Discoveries with Apache Spark

Julien Peloton (CNRS)
  • Data & ML Research
  • 40 MINS
  • Room: E102
16:30

Apache Spark Side of Funnels

Zoran Stipanicev (GetYourGuide)
  • Developer
  • 40 MINS
  • Room: Auditorium
16:30

Building A Feature Factory

Daniel Tomes (Databricks)
  • データサイエンス
  • 40 MINS
  • Room: G102
16:30

Cosmos DB Real-time Advanced Analytics Workshop

Srilakshmi Chintala (Microsoft)
  • チュートリアル
  • 40 MINS
  • Room: G104
16:30

Databricks Delta Lake and Its Benefits

Nitin Raj Soundararajan (Cognizant Worldwide Limited) Nagaraj Sengodan (HCL Technologies)
  • Developer
  • 40 MINS
  • Room: Elicium 2
16:30

Deep Anomaly Detection from Research to Production Leveraging Spark and Tensorflow

Davit Bzhalava (Swedbank) Shaheer Mansoor (Swedbank)
  • AI
  • 40 MINS
  • Room: Emerald
16:30

Driver Location Intelligence at Scale using Apache Spark, Delta Lake, and MLflow on Databricks

Sergio Ballesteros Solanas (TomTom) Kia Eisinga (TomTom)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
16:30

Introduction to TensorFlow 2.0 Brad Miro

Brad Miro (Google)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
16:30

変更データキャプチャを使用した最新の ETL パイプライン

Thiago Rigo (GetYourGuide) David Mariassy (GetYourGuide)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
16:30

Spark Operator—Deploy, Manage and Monitor Spark clusters on Kubernetes

Jiri Kremser (Red Hat, Inc.)
  • Developer
  • 40 MINS
  • Room: E103
17:00

Expo Hall Open

  • ミートアップ
  • 120 MINS
  • Room: Expo Hall
17:00

Healthcare and Life Sciences Networking Event

  • ミートアップ
  • 120 MINS
  • Room: D203
17:00

Retail Networking Event

  • ミートアップ
  • 120 MINS
  • Room: D201
17:20

Accelerating Apache Spark with Intel QuickAssist Technology

Qi Xie (Intel)
  • Data & ML Research
  • 40 MINS
  • Room: E102
17:20

Application and Challenges of Streaming Analytics and Machine Learning on Multi-Variate Time Series Data for Smart Manufacturing

Pranav Prakash (Quartic.ai)
  • AI
  • 40 MINS
  • Room: Emerald
17:20

Astronomical Data Processing on the LSST Scale with Apache Spark

Petar Zecevic (SV Group d.o.o.) Mario Juric (University of Washington)
  • Developer
  • 40 MINS
  • Room: E103
17:20

Best Practices for Building and Deploying Data Pipelines in Apache Spark

Vicky Avison (Cox Automotive UK) Alex Bush (KPMG Lighthouse)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
17:20

Cosmos DB Real-time Advanced Analytics Workshop-continues

Srilakshmi Chintala (Microsoft)
  • チュートリアル
  • 40 MINS
  • Room: G104
17:20

Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonomous Driving

Gheorghe Pucea (BMW Group) Jennifer Reinelt (BMW Group)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
17:20

Apache SparkとElasticsearchを使用したスケーラブルな時系列予測とモニタリング

Andreu Mora (Adyen)
  • データサイエンス
  • 40 MINS
  • Room: G102
17:20

Seamless End-to-End Production Machine Learning with Seldon and MLflow

Adrián González Martín (Seldon Technologies)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
17:20

Spark SQL Bucketing at Facebook

Cheng Su (Facebook)
  • Developer
  • 40 MINS
  • Room: Elicium 2
17:20

Updates from Project Hydrogen: Unifying State-of-the-Art AI and Big Data in Apache Spark EU

Xingbo Jiang (Databricks)
  • Developer
  • 40 MINS
  • Room: Auditorium
18:00

Women in Unified Analytics Happy Hour

  • ミートアップ
  • 90 MINS
  • Room: Topaz Lounge
19:30

Build. Unify. Celebrate. Attendee Party

  • ミートアップ
  • 180 MINS
  • Room: Amstel Boat House

THURSDAY, OCTOBER 17 – CONFERENCE

08:00

Registration

  • Registration
  • 420 MINS
09:00

Forecasting ‘What-if’ Scenarios in Retail Using ML-Powered Interactive Tools

Johan Vallin (Electrolux)
  • 基調講演
  • 90 MINS
  • Room: Auditorium
09:00

Reinventing Payments at HSBC with a Unified Platform for Data and AI in the Cloud

Alessio Basso (HSBC)
  • 基調講演
  • 90 MINS
  • Room: Auditorium
09:00

Scalable AI for Good

Mark Hamilton (Microsoft) Christina Lee (Microsoft)
  • 基調講演
  • 90 MINS
  • Room: Auditorium
09:00

Simplifying Model Management with MLflow

Matei Zaharia (Databricks) Corey Zumar (Databricks)
  • 基調講演
  • 90 MINS
  • Room: Auditorium
10:00

Expo Hall Open – Day 3

  • ミートアップ
  • 480 MINS
  • Room: Expo Hall
10:30

Break #3

  • Break
  • 30 MINS
10:35

Ask Me Anything (AMA): Delta Lake

  • Break
  • 20 MINS
  • Room: Expo Hall
10:35

Ask Me Anything (AMA): Delta Lake cont

  • Break
  • 20 MINS
  • Room: Expo Hall
11:00

AI-Powered Streaming Analytics for Real-Time Customer Experience

John Haddad (Informatica) Vishwa Belur (Informatica)
  • データエンジニアリング
  • 40 MINS
  • Room: E102
11:00

Delta Lake を使用して大規模で信頼性の高いデータレイクを構築する

Andreas Neumann (Databricks) Tathagata Das (Databricks) Mukul Murthy (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: G104
11:00

Continuous Evaluation of Deployed Models in Production

Deepak Pai (Adobe, Inc.) Vijay Srivastava (Adobe, Inc.)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
11:00

Creating an Omnichannel Banking Experience with Machine Learning on Azure Databricks

Petr Pluhacek (Ceska sporitelna) Jakub Stech (DataSentics a.s.)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
11:00

Detecting Financial Fraud at Scale with Machine Learning

Elena Boiarskaia (H2O.ai)
  • データサイエンス
  • 40 MINS
  • Room: G102
11:00

Graph Features in Spark 3.0: Integrating Graph Querying and Algorithms in Spark Graph

Mats Rydberg (Neo4j) Max Kießling (Neo4j)
  • チュートリアル
  • 40 MINS
  • Room: G104
11:00

How Data is Transforming the Dutch Media Industry

Maurits van der Goes (RTL Netherlands)
  • AI
  • 40 MINS
  • Room: Emerald
11:00

Performance Troubleshooting Using Apache Spark Metrics

Luca Canali (CERN)
  • Developer
  • 40 MINS
  • Room: Elicium 2
11:00

Petabytes, Exabytes, and Beyond: Managing Delta Lakes for Interactive Queries at Scale

Christopher Hoshino-Fish (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
11:00

The Internals of Stateful Stream Processing in Spark Structured Streaming

Jacek Laskowski (Development and training services)
  • Developer
  • 40 MINS
  • Room: E103
11:00

The Parquet Format and Performance Optimization Opportunities

Boudewijn Braams (Databricks)
  • Developer
  • 40 MINS
  • Room: Auditorium
11:00

機械学習モデルを解釈するための統合アプローチ：SHAP + LIME

Layla Yang (Databricks)
  • データサイエンス
  • 40 MINS
  • Room: E107
11:50

ACID ORC, Iceberg, and Delta Lake—An Overview of Table Formats for Large Scale Storage and Analytics

Michal Gancarski (Zalando SE)
  • Developer
  • 40 MINS
  • Room: Auditorium
11:50

Applied Machine Learning for Ranking Products in an Ecommerce Setting

Arnoud de Munnik (Wehkamp) Jerry Vos (Wehkamp)
  • データサイエンス
  • 40 MINS
  • Room: G102
11:50

Augmenting Machine Learning with Databricks Labs AutoML Toolkit

Denny Lee (Databricks) Mary Grace Moesta (Databricks)
  • データサイエンス
  • 40 MINS
  • Room: E107
11:50

Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets

Harish Kumar (RB)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
11:50

Data Warehousing with Spark Streaming at Zalando

Sebastian Herold (Zalando SE)
  • データエンジニアリング
  • 40 MINS
  • Room: E103
11:50

Drug Discovery and Development Using AI

Vishnu Vettrivel (Wisecube AI)
  • AI
  • 40 MINS
  • Room: Emerald
11:50

Databricks Deltaを使用したエンドツーエンドのSpark/TensorFlow/PyTorchパイプライン

Jim Dowling (Logical Clocks AB) Kim Hammar (Logical Clocks AB)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
11:50

Graph Features in Spark 3.0: Integrating Graph Querying and Algorithms in Spark Graph—continues

Mats Rydberg (Neo4j) Max Kießling (Neo4j)
  • チュートリアル
  • 40 MINS
  • Room: G104
11:50

Industrializing Machine Learning on an Enterprise Azure Platform with Databricks: Experiences and Feedbacks

Yannick Radji (Sodexo)
  • Developer
  • 40 MINS
  • Room: Elicium 2
11:50

Powering Custom Apps at Facebook using Spark Script Transformation

Abdulrahman Alfozan (Facebook)
  • データエンジニアリング
  • 40 MINS
  • Room: E102
11:50

Stream, Stream, Stream: Different Streaming Methods with Apache Spark and Kafka

Itai Yaffe (Imply)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
11:50

Women in Unified Data Analytics Lunch

  • Lunch
  • 100 MINS
  • Room: D203
12:30

Lunch – Day 3

  • Lunch
  • 60 MINS
13:30

Democratizing Machine Learning: Perspective from a scikit-learn Creator

Gaël Varoquaux (Inria)
  • 基調講演
  • 60 MINS
  • Room: Auditorium
13:30

Imaging the Unseen: Taking the First Picture of a Black Hole

Katie Bouman (California Institute of Technology)
  • 基調講演
  • 60 MINS
  • Room: Auditorium
14:40

Accelerating Apache Spark by Several Orders of Magnitude with GPUs and RAPIDS Library

Miguel Martinez (NVIDIA) Thomas Graves (NVIDIA)
  • データサイエンス
  • 40 MINS
  • Room: G102
14:40

Apache Spark Core – Practical Optimization

Daniel Tomes (Databricks)
  • Developer
  • 40 MINS
  • Room: Auditorium
14:40

CyberMLToolkit: Anomaly Detection as a Scalable Generic Service Over Apache Spark

Roy Levin (Microsoft)
  • AI
  • 40 MINS
  • Room: Emerald
14:40

Data Democratization at Nubank

Andre Jasiskis (Nubank) Rodrigo Ney (Nubank)
  • データエンジニアリング
  • 40 MINS
  • Room: E103
14:40

From HelloWorld to Configurable and Reusable Apache Spark Applications in Scala – A Developer’s Journey

Oliver Tupran (Devoteam)
  • Developer
  • 40 MINS
  • Room: Elicium 2
14:40

Managing the Complete Machine Learning Lifecycle with MLflow EU

Thunder Shiviah (Databricks) Michael Shtelma (Databricks)
  • チュートリアル
  • 40 MINS
  • Room: G104
14:40

On-Prem Solution for the Selection of Wind Energy Models

Ana Maria Martinez Fernandez (Vestas)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
14:40

Performance Analysis of Apache Spark and Presto in Cloud Environments

Victor Cuevas-Vicenttin (Barcelona Super Computing)
  • データエンジニアリング
  • 40 MINS
  • Room: E107
14:40

Reliable Performance at Scale with Apache Spark on Kubernetes

Will Manning (Palantir) Matthew Cheah (Palantir)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
14:40

Revolutionizing the Legal Industry with Spark, NLP and Azure Databricks at Clifford Chance

Mirko Bernardoni (Clifford Chance) Michael Seddon (Clifford Chance)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
14:40

Tactical Data Science Tips: Python and Spark Together

Bill Chambers (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: E102
15:30

Accelerating Apache Spark by Several Orders of Magnitude with GPUs and RAPIDS Library-continues

Miguel Martinez (NVIDIA) Thomas Graves (NVIDIA)
  • データサイエンス
  • 40 MINS
  • Room: G102
15:30

Apache Spark Core – Practical Optimization —continues

Daniel Tomes (Databricks)
  • Developer
  • 40 MINS
  • Room: Auditorium
15:30

Building Reliable Data Lakes at Scale with Delta Lake – continued

Andreas Neumann (Databricks) Tathagata Das (Databricks) Mukul Murthy (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
15:30

High-Performance Advanced Analytics with Spark-Alchemy

Simeon Simeonov (Swoop)
  • Developer
  • 40 MINS
  • Room: E107
15:30

Improving the Life of Data Scientists: Automating ML Lifecycle through MLflow

Subru Krishnan (Microsoft) Avrilia Floratou (Microsoft)
  • AI
  • 40 MINS
  • Room: Emerald
15:30

Managing the Complete Machine Learning Lifecycle with MLflow—continues

Thunder Shiviah (Databricks) Michael Shtelma (Databricks)
  • チュートリアル
  • 40 MINS
  • Room: G104
15:30

Maps and Meaning: Graph-based Entity Resolution in Apache Spark & GraphX

Hendrik Frentrup (Systemati.co)
  • データエンジニアリング
  • 40 MINS
  • Room: E102
15:30

Powering Asurion’s Connected Home Platform with Spark Structured Streaming, Delta Lake, and MLflow

Tomasz Magdanski (Asurion) Shobhit Gupta (Asurion)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
15:30

Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Analytics with Spark AI

Benyue (Emma) Liu (TigerGraph)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
15:30

Simplify and Scale Data Engineering Pipelines with Delta Lake

Amanda Moran (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: E103
15:30

Working with Complex Types in DataFrames: Optics to the Rescue

Alfonso Roa Redondo (Habla computing)
  • Developer
  • 40 MINS
  • Room: Elicium 2
16:10

Break #4

  • Break
  • 30 MINS
16:15

Ask Me Anything (AMA): MLflow

  • Break
  • 20 MINS
  • Room: Expo Hall
16:40

Auto-Pilot for Apache Spark Using Machine Learning

Amogh Margoor (Qubole) Urvashi Kohli (Qubole) Mayur Bhosale (Qubole)
  • AI
  • 40 MINS
  • Room: Emerald
16:40

Blue Pill/Red Pill: The Matrix of Thousands of Data Streams

Himanshu Gupta (Knoldus Inc.)
  • Developer
  • 40 MINS
  • Room: Elicium 2
16:40

Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks

Akhil Dhingra (Zalando) Saurav Verma (Zalando)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
16:40

Building Reliable Data Lakes at Scale with Delta Lake cont

Andreas Neumann (Databricks) Tathagata Das (Databricks) Mukul Murthy (Databricks)
  • チュートリアル
  • 40 MINS
  • Room: G104
16:40

Deploying End-to-End Deep Learning Pipelines with ONNX

Nick Pentreath (IBM)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
16:40

Enabling Biobank-Scale Genomic Processing with Spark SQL

Karen Feng (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: E107
16:40

Implementing a Reliable Data Lake with Databricks Delta and the AWS Ecosystem

Denis Dubeau (Databricks) Jordan Martz (Qlik)
  • Developer
  • 40 MINS
  • Room: Auditorium
16:40

Improving Apache Spark Downscaling

Christopher Crosbie (Google) Ben Sidhom (Google)
  • データエンジニアリング
  • 40 MINS
  • Room: E102
16:40

Machine Learning at Scale with MLflow and Apache Spark

Chongguang Liu (Société Générale)
  • データサイエンス
  • 40 MINS
  • Room: G102
16:40

MLflow and Azure Machine Learning—The Power Couple for ML Lifecycle Management

Nishant Thacker (Microsoft)
  • データエンジニアリング
  • 40 MINS
  • Room: E103
16:40

Scaling Data Analytics Workloads on Databricks

Bogdan Ghit (Databricks) Chris Stevens (Databricks)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
17:00

Media and Entertainment Networking Event

  • ミートアップ
  • 120 MINS
  • Room: D203
17:30

AI on Spark for Malware Analysis and Anomalous Threat Detection

Jakub Sanojca (Avast) Joao Da Silva (Avast)
  • AI
  • 40 MINS
  • Room: Emerald
17:30

Apache Spark for Cyber Security in an Enterprise Company

Josef Niedermeier (Hewlett Packard Enterprise)
  • Data and ML Industry Use Case
  • 40 MINS
  • Room: G106
17:30

Automated Production Ready ML at Scale

Errol Koolmeister (H&M) Keven Wang (H&M)
  • データサイエンス
  • 40 MINS
  • Room: G102
17:30

Bridging the Gap Between Data Scientists and Software Engineers – Deploying Legacy Python Algorithms to Apache Spark with Minimum Pain

Lucas Partridge (GE Aviation Digital) Peter Knight (GE Aviation)
  • データエンジニアリング
  • 40 MINS
  • Room: Forum
17:30

Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas

Thunder Shiviah (Databricks)
  • データサイエンス
  • 40 MINS
  • Room: E107
17:30

Improving Apache Spark Downscaling—continues

Christopher Crosbie (Google) Ben Sidhom (Google)
  • データエンジニアリング
  • 40 MINS
  • Room: E102
17:30

Listening at the Cocktail Party with Deep Neural Networks and TensorFlow

Christian Grant (Demant)
  • データサイエンス
  • 40 MINS
  • Room: Elicium 1
17:30

Optimizing Delta/Parquet Data Lakes for Apache Spark EU

Matthew Powers (Prognos)
  • データエンジニアリング
  • 40 MINS
  • Room: E103
17:30

Refactoring Apache Spark to Allow Additional Cluster Managers

Kevin Doyle (IBM)
  • Developer
  • 40 MINS
  • Room: Auditorium
17:30

Using Production Profiles to Guide Optimizations

Adam Barth (Facebook)
  • Developer
  • 40 MINS
  • Room: Elicium 2