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 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)
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Add Machine learning in R to your personal schedule
9:00am Machine learning in R Jared Lander (Lander Analytics)
Add Unraveling data with Spark using deep learning and other algorithms from machine learning to your personal schedule
1:30pm Unraveling data with Spark using deep learning and other algorithms from machine learning Vartika Singh (Cloudera), Jeffrey Shmain (Cloudera)
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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), Karthik Bharadwaj Thirumalai (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), Mike Prorock (mesur.io), Hugo Sheng (Qlik), Neil Hirano (Dotz), Leandro Andrade (Dotz), Praveen Deorani (Holmusk), 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 Group), Mark Donsky (Cloudera), Syed Rafice (Cloudera), Victor Chua (StarHub Ltd), Carme Artigas (Synergic Partners), Zhihao Lin (Teralytics)
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
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 explains 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. Interactive visualizations 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. Jared Lander walks you through the available methods for implementing machine learning algorithms in R and explores underlying theories such as the elastic net, boosted trees, and cross-validation.
1:30pm-5:00pm (3h 30m) Machine Learning, Spark and beyond
Unraveling data with Spark using deep learning and other algorithms from machine learning
Vartika Singh (Cloudera), Jeffrey Shmain (Cloudera)
Vartika Singh and Jeffrey Shmain walk you through various approaches using the machine learning algorithms available in Spark ML to understand and decipher meaningful patterns in real-world data. Vartika and Jeff also demonstrate how to leverage open source deep learning frameworks to run classification problems on image and text datasets leveraging Spark.
9:00am-12:30pm (3h 30m) Data science and advanced analytics, Machine Learning
Getting started with TensorFlow
Yufeng Guo (Google)
Yufeng Guo walks you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng takes you from a conceptual overview all the way to building complex classifiers and explains how you can apply deep learning 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), Karthik Bharadwaj Thirumalai (Teradata)
Tim Seears and Karthik Bharadwaj Thirumalai explain how to apply deep learning to improve consumer recommendations by training neural nets to learn categories of interest using embeddings. They then 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), Mike Prorock (mesur.io), Hugo Sheng (Qlik), Neil Hirano (Dotz), Leandro Andrade (Dotz), Praveen Deorani (Holmusk), 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 Group), Mark Donsky (Cloudera), Syed Rafice (Cloudera), Victor Chua (StarHub Ltd), Carme Artigas (Synergic Partners), Zhihao Lin (Teralytics)
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