Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore
 
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Add A deep dive into running big data workloads in the cloud  to your personal schedule
9:00am A deep dive into running big data workloads in the cloud Vinithra Varadharajan (Cloudera), Philip Langdale (Cloudera), Jason Wang (Cloudera), Fahd Siddiqui (Cloudera)
Add Architecting a next-generation data platform to your personal schedule
1:30pm Architecting a next-generation data platform Jonathan Seidman (Cloudera), Ted Malaska (Blizzard Entertainment)
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Add Developing a modern enterprise data strategy to your personal schedule
9:00am Developing a modern enterprise data strategy Edd Wilder-James (Google), John Akred (Silicon Valley Data Science)
Add Interactive visualization for data science to your personal schedule
1:30pm Interactive visualization for data science Bargava Subramanian (Independent), Amit Kapoor (narrativeVIZ Consulting)
321/322
Add Machine Learning in R to your personal schedule
9:00am Machine Learning in R Jared Lander (Lander Analytics)
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Add Getting started with TensorFlow to your personal schedule
9:00am Getting started with TensorFlow Yufeng Guo (Google)
Add Deep learning for recommender systems to your personal schedule
1:30pm Deep learning for recommender systems Tim Seears (Think Big, a Teradata company), David Mueller (Teradata)
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Add Data Case Studies to your personal schedule
9:00am Data Case Studies Alistair Croll (Solve For Interesting), kyungtaak Noh (SK Telecom), Jisung Kim (SK Telecom), Mike Prorock (mesur.io), Hugo Sheng (Qlik), Alexandre Chade (Dotz), Jonathan Seidman (Cloudera), Ted Malaska (Blizzard Entertainment), Mike Koelemay (Sikorsky Aircraft, Lockheed Martin)
Add Smart Cities to your personal schedule
1:30pm Smart Cities Alistair Croll (Solve For Interesting), Clifton Phua (NCS Pte Ltd), Mark Donsky (Cloudera), Syed Rafice (Cloudera), Victor Chua (StarHub Ltd), Isaac Reyes (DataSeer)
12:30pm Lunch | Room: Summit 1 & 2
8:30am Coffee Break | Room: Foyer 3 & 5
10:30am Morning break | Room: Foyer 3 & 5
3:00pm Afternoon break | Room: Foyer 3 & 5
9:00am-12:30pm (3h 30m) Big data and the cloud
A deep dive into running big data workloads in the cloud
Vinithra Varadharajan (Cloudera), Philip Langdale (Cloudera), Jason Wang (Cloudera), Fahd Siddiqui (Cloudera)
Vinithra Varadharajan, Philip Langdale, Jason Wang, and Fahd Siddiqui lead a deep dive into running data engineering workloads in a managed service capacity in the public cloud, highlighting cloud infrastructure best practices and illustrating how data engineering workloads interoperate with data analytic engines.
1:30pm-5:00pm (3h 30m) Data engineering and architecture
Architecting a next-generation data platform
Jonathan Seidman (Cloudera), Ted Malaska (Blizzard Entertainment)
Using Customer 360 and the IoT as examples, Jonathan Seidman and Ted Malaska explain how to architect a modern, real-time big data platform leveraging recent advancements in the open source software world, using components like Kafka, Impala, Kudu, Spark Streaming, and Spark SQL with Hadoop to enable new forms of data processing and analytics.
9:00am-12:30pm (3h 30m) Becoming a data-centric company, Strata Business Summit
Developing a modern enterprise data strategy
Edd Wilder-James (Google), John Akred (Silicon Valley Data Science)
Big data, AI, and data science have great potential for accelerating business, but how do you reconcile business opportunity with the sea of possible technologies? Data should serve the strategic imperatives of a business—those aspirations that will define an organization’s future vision. John Akred and Edd Wilder-James explain how to create a modern data strategy that powers data-driven business.
1:30pm-5:00pm (3h 30m) Design, UX, visualization, and VR, Machine Learning
Interactive visualization for data science
Bargava Subramanian (Independent), Amit Kapoor (narrativeVIZ Consulting)
One of the challenges in traditional data visualization is that they are static and have bounds on limited physical/pixel space. Interaction allows us to move beyond this limitation by adding layers of interactions. Bargava Subramanian and Amit Kapoor teach the art and science of creating interactive data visualizations.
9:00am-12:30pm (3h 30m) Data science and advanced analytics, Machine Learning
Machine Learning in R
Jared Lander (Lander Analytics)
Modern statistics has become almost synonymous with machine learning; a collection of techniques that utilize today's incredible computing power. This course focuses on the available methods for implementing machine learning algorithms in R, and will examine some of the underlying theories behind the curtain, covering the Elastic Net, Boosted Trees and cross-validation.
1:30pm-5:00pm (3h 30m) Machine Learning, Spark and beyond
Unravelling data at scale with Spark using deep learning and other algorithms from machine learning.
Vartika Singh (Cloudera), Jeffrey Shmain (Cloudera)
We walk you through approaches available via machine-learning algorithms available in Spark ml to understand and decipher meaningful patterns in real-world data. Along with discussing the common problems encountered as the data and model sizes scale we will also leverage a few open source deep learning frameworks to run a few classification problems on image and text data sets leveraging Spark.
9:00am-12:30pm (3h 30m) Data science and advanced analytics, Machine Learning
Getting started with TensorFlow
Yufeng Guo (Google)
We will walk you through training and deploying a machine-learning system using TensorFlow, a popular open source ML library. Starting from conceptual overviews, we will build all the way up to complex classifiers. You’ll gain insight into deep learning and how it can apply to complex problems in science and industry.
1:30pm-5:00pm (3h 30m) Data science and advanced analytics, Machine Learning
Deep learning for recommender systems
Tim Seears (Think Big, a Teradata company), David Mueller (Teradata)
Tim Seears and David Mueller explain how to apply deep learning to improve consumer recommendations by training neural nets to learn categories of interest using embeddings and demonstrate how to extend this with WALS matrix factorization to achieve wide and deep learning—a process which is now used in production for the Google Play Store.
9:00am-12:30pm (3h 30m)
Data Case Studies
Alistair Croll (Solve For Interesting), kyungtaak Noh (SK Telecom), Jisung Kim (SK Telecom), Mike Prorock (mesur.io), Hugo Sheng (Qlik), Alexandre Chade (Dotz), Jonathan Seidman (Cloudera), Ted Malaska (Blizzard Entertainment), Mike Koelemay (Sikorsky Aircraft, Lockheed Martin)
In a series of half-hour talks aimed at a business audience, you’ll hear from household brands and global companies as they explain the challenges they wanted to tackle, the approaches they took, and the benefits—and drawbacks—of their solutions. If you want practical insights about applied data, look no further.
1:30pm-5:00pm (3h 30m)
Smart Cities
Alistair Croll (Solve For Interesting), Clifton Phua (NCS Pte Ltd), Mark Donsky (Cloudera), Syed Rafice (Cloudera), Victor Chua (StarHub Ltd), Isaac Reyes (DataSeer)
The modern city is awash in data. Cheap sensors on cars, roads, and people give us a real-time understanding of traffic. We can track pollution, temperature, and climate with unerring precision. Satellite photographs reveal shade cover, property values, and building development.
12:30pm-1:30pm (1h)
Break: Lunch
8:30am-9:00am (30m)
Break: Coffee Break
10:30am-11:00am (30m)
Break: Morning break
3:00pm-3:30pm (30m)
Break: Afternoon break