Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore
 
Summit 1
11:15am Engineering cloud-native machine learning applications Harjindersingh Mistry (Ola), Bargava Subramanian (Binaize Labs)
12:05pm Making R go faster and bigger Jared Lander (Lander Analytics)
2:35pm The trials of machine learning at Zendesk Wai Chee Yau (Zendesk), Jeffrey Theobald (Zendesk)
Summit 2
11:15am TensorFlow: Open source machine learning Wolff Dobson (Google)
12:05pm Bootstrap custom image classification using transfer learning Danielle Dean (iRobot), Wee Hyong Tok (Microsoft)
1:45pm A recommendation system for wide transactions Bargava Subramanian (Binaize Labs), Harjindersingh Mistry (Ola)
2:35pm Data production pipelines: Legacy, practices, and innovation Natalino Busa (DBS), Matteo Pelati (DataRobot)
5:05pm Energy monitoring with a self-taught deep network Yiqun Hu (Singapore Power)
308/309
11:15am Top five mistakes when writing streaming applications Ted Malaska (Capital One)
2:35pm Big telco real-time network analytics Yousun Jeong (SK Telecom)
5:05pm Streaming analytics at Grab Andreas Hadimulyono (Grab)
310/311
11:15am Apache Spark in the hands of data scientists Neelesh Salian (Stitch Fix)
12:05pm How to successfully run data pipelines in the cloud Kostas Sakellis (Cloudera), Philip Langdale (Cloudera)
1:45pm High-performance enterprise data processing with Spark Vickye Jain (ZS Associates), Raghav Sharma (ZS Associates)
2:35pm TigerGraph: A complete high-performance graph data and analytics platform Mingxi Wu (TigerGraph), Yu Xu (TigerGraph)
4:15pm Operationalizing Presto in the cloud: Lessons and mistakes Feng Cheng (Grab), Yanyu Qu (Grab)
321/322
11:15am Organizing for machine learning success John Akred (Silicon Valley Data Science), Mark Hunter (Sainsburys Bank)
12:05pm The art of data storytelling Isaac Reyes (DataSeer)
2:35pm Practical applications for graph techniques in supply chain analysis and finance Eric Tham (National University of Singapore), Radha Pendyala (Thomson Reuters)
4:15pm The value of a data science center of excellence (COE) Benjamin Wright-Jones (Microsoft), Simon Lidberg (Microsoft)
5:05pm Managing machine learning models in production Anand Chitipothu (rorodata)
328/329
12:05pm Executive Briefing: The five dysfunctions of a data engineering team Jesse Anderson (Big Data Institute)
1:45pm Executive Briefing: The data-driven growth engine Jessica Chen Riolfi (TransferWise)
4:15pm Executive Briefing: The business case for AI, Spark, and friends John Akred (Silicon Valley Data Science)
323
12:05pm Designing AI-based conversational UIs Mohammed Abdoolcarim (Vahan)
2:35pm Talent Flow Behaviour Analytics: A Data Driven Approach to Human Capital Management Philips PRASETYO (Living Analytics Research Centre, Singapore Management University), Ee-Peng Lim (Singapore Management University)
4:15pm Privacy by design, not an afterthought: Best practices at LinkedIn Shirshanka Das (LinkedIn), Tushar Shanbhag (LinkedIn)
5:05pm GDPR: Getting your data ready for heavy, new EU privacy regulations Mark Donsky (Okera), Steven Ross (Cloudera)
334/335
Hall 404AXF
8:50am Wednesday keynote welcome Ben Lorica (O'Reilly), Doug Cutting (Cloudera), Alistair Croll (Solve For Interesting)
9:00am Computational challenges and opportunities of astronomical big data Melanie Johnston-Hollitt (Victoria University of Wellington)
9:15am Siri: The journey to consolidation Mick Hollison (Cloudera), Cesar Delgado (Apple)
9:30am Technology for humanity Steve Leonard (SGInnovate)
9:45am Responsible deployment of machine learning Ben Lorica (O'Reilly)
10:00am Industrial machine learning Joshua Bloom (GE Digital)
8:00am Coffee break sponsored by TigerGraph | Room: Hall 404 Foyer
8:15am Speed Networking | Room: Hall 404 Foyer
10:45am Morning break sponsored by Google | Room: Sponsor Pavilion, Concourse 1-4
12:45pm Wednesday Topic Tables at Lunch (Located in room 324) | Room: Sponsor Pavilion, Concourse 1-4
3:15pm Afternoon break | Room: Sponsor Pavilion, Concourse 1-4
5:45pm Sponsor Pavilion Reception | Room: Sponsor Pavilion, Concourse 1-4
11:15am-11:55am (40m) Data engineering and architecture, Machine Learning
Engineering cloud-native machine learning applications
Harjindersingh Mistry (Ola), Bargava Subramanian (Binaize Labs)
In the current Agile business environment, where developers are required to experiment multiple ideas and also react to various situations, doing cloud-native development is the way to go. Harjinder Mistry and Bargava Subramanian explain how to design and build a microservices-based cloud-native machine learning application.
12:05pm-12:45pm (40m) Data engineering and architecture, Data science and advanced analytics, Machine Learning
Making R go faster and bigger
Jared Lander (Lander Analytics)
One common (but false) knock against R is that it doesn't scale well. Jared Lander shows how to use R in a performant matter both in terms of speed and data size and offers an overview of packages for running R at scale.
1:45pm-2:25pm (40m) Data engineering and architecture, Data science and advanced analytics, Machine Learning
Train, predict, and serve: How to put your machine learning model into production
Aki Ariga (Cloudera)
Aki Ariga explains how to put your machine learning model into production, discusses common issues and obstacles you may encounter, and shares best practices and typical architecture patterns of deployment ML models with example designs from the Hadoop and Spark ecosystem using Cloudera Data Science Workbench.
2:35pm-3:15pm (40m) Data engineering and architecture, Machine Learning
The trials of machine learning at Zendesk
Wai Chee Yau (Zendesk), Jeffrey Theobald (Zendesk)
Simply building a successful machine learning product is extremely challenging, and just as much effort is needed to turn that model into a customer-facing product. Drawing on their experience working on Zendesk's article recommendation product, Wai Yau and Jeffrey Theobald discuss design challenges and real-world problems you may encounter when building a machine learning product at scale.
4:15pm-4:55pm (40m) Data engineering and architecture, Machine Learning, Spark and beyond
Extending Spark ML: Adding custom pipeline stages to Spark
Holden Karau (Independent)
Apache Spark’s machine learning (ML) pipelines provide a lot of power, but sometimes the tools you need for your specific problem aren’t available yet. Holden Karau introduces Spark’s ML pipelines and explains how to extend them with your own custom algorithms, allowing you to take advantage of Spark's meta-algorithms and existing ML tools.
5:05pm-5:45pm (40m) Data engineering and architecture, Machine Learning, Spark and beyond
Apache Spark ML and MLlib tuning and optimization: A case study on boosting the performance of ALS by 60x
Peng Meng (Intel)
Apache Spark ML and MLlib are hugely popular in the big data ecosystem, and Intel has been deeply involved in Spark from a very early stage. Peng Meng outlines the methodology behind Intel's work on Spark ML and MLlib optimization and shares a case study on boosting the performance of Spark MLlib ALS by 60x in JD.com’s production environment.
11:15am-11:55am (40m) Data science and advanced analytics, Machine Learning
TensorFlow: Open source machine learning
Wolff Dobson (Google)
TensorFlow, the world's most popular machine learning framework, is fast, flexible, and production ready. Wolff Dobson, representing the Google Brain team, shares the latest developments in TensorFlow, including tensor processing units (TPUs), distributed training, new APIs and models, and mobile features. Join in to learn what's in store for TensorFlow and how ML can change your business.
12:05pm-12:45pm (40m) Data science and advanced analytics, Machine Learning
Bootstrap custom image classification using transfer learning
Danielle Dean (iRobot), Wee Hyong Tok (Microsoft)
Transfer learning enables you to use pretrained deep neural networks (e.g., AlexNet, ResNet, and Inception V3) and adapt them for custom image classification tasks. Danielle Dean and Wee Hyong Tok walk you through the basics of transfer learning and demonstrate how you can use the technique to bootstrap the building of custom image classifiers.
1:45pm-2:25pm (40m) Data science and advanced analytics, Machine Learning
A recommendation system for wide transactions
Bargava Subramanian (Binaize Labs), Harjindersingh Mistry (Ola)
Bargava Subramanian and Harjinder Mistry share data engineering and machine learning strategies for building an efficient real-time recommendation engine when the transaction data is both big and wide. They also outline a novel way of generating frequent patterns using collaborative filtering and matrix factorization on Apache Spark and serving it using Elasticsearch in the cloud.
2:35pm-3:15pm (40m) Data engineering and architecture, Machine Learning
Data production pipelines: Legacy, practices, and innovation
Natalino Busa (DBS), Matteo Pelati (DataRobot)
Modern engineering requires machine learning engineers, who are needed to monitor and implement ETL and machine learning models in production. Natalino Busa shares technologies, techniques, and blueprints on how to robustly and reliably manage data science and ETL flows from inception to production.
4:15pm-4:55pm (40m) Big data and the cloud, Machine Learning
TensorFlow wide and deep: Data classification the easy way
Yufeng Guo (Google)
Yufeng Guo demonstrates how to use TensorFlow to easily combine linear regression models and deep neural networks with a machine learning model that has the benefits of both. You'll also learn what is happening under the hood and how you can use this model for your own datasets.
5:05pm-5:45pm (40m) Machine Learning
Energy monitoring with a self-taught deep network
Yiqun Hu (Singapore Power)
Energy usage is a significant part of daily life, so the ability to monitor this use offers a number of benefits, from cost savings to improved safety. A key challenge is the lack of labeled data. Yiqun Hu shares a new solution: a RNN-based network trained to learn good features from unlabeled data.
11:15am-11:55am (40m) Data engineering and architecture
Top five mistakes when writing streaming applications
Ted Malaska (Capital One)
Ted Malaska shares the top five mistakes that no one talks about when you start writing your streaming app along with the practices you'll inevitably need to learn along the way.
12:05pm-12:45pm (40m)
Analytics at ING: Technology solutions to create a real-time, data-driven bank
Bas Geerdink (Aizonic)
Bas Geerdink explains why and how ING is becoming more and more data-driven, sharing use cases, architecture, and technology choices along the way.
1:45pm-2:25pm (40m) Data engineering and architecture, Stream processing and analytics
From Kafka to BigQuery: A guide for delivering billions of daily events
Ofir Sharony (MyHeritage)
What are the most important considerations for shipping billions of daily events to analysis? Ofir Sharony shares MyHeritage's journey to find a reliable and efficient way to achieve real-time analytics. Along the way, Ofir compares several data loading techniques, helping you make better choices when building your next data pipeline.
2:35pm-3:15pm (40m) Big data in telecommunications, Data engineering and architecture
Big telco real-time network analytics
Yousun Jeong (SK Telecom)
Data transfer is one of the most pressing problems for telecom companies, as cost increases in tandem with the growing data requirements. Yousun Jeong details how SKT has dealt with this problem.
4:15pm-4:55pm (40m) Data engineering and architecture, IoT and intelligent real-time applications
Spark Structured Streaming helps smart manufacturing
Xiaochang Wu (Intel)
Xiaochang Wu explains how to design and implement a real-time processing platform using the Spark Structured Streaming framework to intelligently transform production lines in the manufacturing industry.
5:05pm-5:45pm (40m) Data case studies, Data engineering and architecture
Streaming analytics at Grab
Andreas Hadimulyono (Grab)
Andreas Hadimulyono discusses the challenges that Grab is facing with the ever-increasing volume and velocity of its data and shares the company's plans to overcome them.
11:15am-11:55am (40m) Data engineering and architecture
Apache Spark in the hands of data scientists
Neelesh Salian (Stitch Fix)
Neelesh Srinivas Salian offers an overview of the data platform used by data scientists at Stitch Fix, based on the Spark ecosystem. Neelesh explains the development process and shares some lessons learned along the way.
12:05pm-12:45pm (40m)
How to successfully run data pipelines in the cloud
Kostas Sakellis (Cloudera), Philip Langdale (Cloudera)
With its scalable data store, elastic compute, and pay-as-you-go cost model, cloud infrastructure is well-suited for large-scale data engineering workloads. Kostas Sakellis explores the latest cloud technologies, focusing on data engineering workloads, cost, security, and ease-of-use implications for data engineers.
1:45pm-2:25pm (40m) Data engineering and architecture
High-performance enterprise data processing with Spark
Vickye Jain (ZS Associates), Raghav Sharma (ZS Associates)
Vickye Jain and Raghav Sharma explain how they built a very high-performance data processing platform powered by Spark that balances the considerations of extreme performance, speed of development, and cost of maintenance.
2:35pm-3:15pm (40m) Data engineering and architecture
TigerGraph: A complete high-performance graph data and analytics platform
Mingxi Wu (TigerGraph), Yu Xu (TigerGraph)
Mingxi Wu and Yu Xu offer an overview of TigerGraph, a high-performance enterprise graph data platform that enables businesses to transform structured, semistructured, and unstructured data and massive enterprise data silos into an intelligent interconnected data network, allowing them to uncover the implicit patterns and critical insights to drive business growth.
4:15pm-4:55pm (40m) Big data and the cloud, Data engineering and architecture
Operationalizing Presto in the cloud: Lessons and mistakes
Feng Cheng (Grab), Yanyu Qu (Grab)
Grab uses Presto to support operational reporting (batch and near real-time), ad hoc analyses, and its data pipeline. Currently, Grab has 5+ clusters with 100+ instances in production on AWS and serves up to 30K queries per day while supporting more than 200 internal data users. Feng Cheng and Yanyu Qu explain how Grab operationalizes Presto in the cloud and share lessons learned along the way.
5:05pm-5:45pm (40m) Big data and the cloud, Data engineering and architecture
Rethinking data marts in the cloud: Common architectural patterns for analytics
Greg Rahn (Cloudera)
Cloud environments will likely play a key role in your business’s future. Henry Robinson and Greg Rahn explore the workload considerations when evaluating the cloud for analytics and discuss common architectural patterns to optimize price and performance.
11:15am-11:55am (40m) Becoming a data-centric company, Strata Business Summit
Organizing for machine learning success
John Akred (Silicon Valley Data Science), Mark Hunter (Sainsburys Bank)
Deploying machine learning in business requires far more than just selecting an algorithm. You need the right architecture, tools, and team organization to drive your agenda successfully. John Akred and Mark Hunter share practical advice on the technical and human sides of machine learning, based on experience preparing Sainsbury’s for its ML-enabled future.
12:05pm-12:45pm (40m) Design, UX, visualization, and VR, Strata Business Summit
The art of data storytelling
Isaac Reyes (DataSeer)
Isaac Reyes explores the art and science of data storytelling, covering the essential elements of a good data story, chart design and why it matters, the Gestalt principals of visual perception and how they can be used to tell better stories with data, and how to make over a poor visualization.
1:45pm-2:25pm (40m) Financial technology and data, Strata Business Summit
Driving financial inclusion in emerging markets using alternate data and big data analytics
Amit Das (Think Analytics India)
Access to credit in emerging markets is impeded by issues around identity verification, risk assessment and monitoring, and the costs of underwriting and collections. At the core of it all is a lack of data. Amit Das explains how accessing alternate data, real-time risk monitoring and data access solutions, and smart analytics is changing the lending landscape in India.
2:35pm-3:15pm (40m) Data case studies, Strata Business Summit
Practical applications for graph techniques in supply chain analysis and finance
Eric Tham (National University of Singapore), Radha Pendyala (Thomson Reuters)
Graphical techniques are increasingly being used for big data. These techniques can be broadly classified into the three C's: centrality, clustering, and connectedness. Eric Tham explains how these concepts are applied to supply chain analysis and financial portfolio management.
4:15pm-4:55pm (40m) Becoming a data-centric company, Strata Business Summit
The value of a data science center of excellence (COE)
Benjamin Wright-Jones (Microsoft), Simon Lidberg (Microsoft)
As organizations turn to data-driven strategies, they are also increasingly exploring the creation of a data science or analytic center of excellence (COE). Benjamin Wright-Jones and Simon Lidberg outline the building blocks of a center of excellence and describe the value for organizations embarking on data-driven strategies.
5:05pm-5:45pm (40m) Data science and advanced analytics, Machine Learning
Managing machine learning models in production
Anand Chitipothu (rorodata)
There are many challenges to deploying machine models in production, including managing multiple versions of models, maintaining staging and production models, keeping track of model performance, logging, and scaling. Anand Chitipothu explores the tools, techniques, and system architecture of a cloud platform built to solve these challenges and the new opportunities it opens up.
11:15am-11:55am (40m) Strata Business Summit
Executive Briefing: Artificial intelligence—The next digital frontier?
Sachin Chitturu (McKinsey & Company)
After decades of extravagant promises, artificial intelligence is finally starting to deliver real-life benefits to early adopters. However, we're still early in the cycle of adoption. Shilpa Aggarwal explains where investment is going, patterns of AI adoption, and how the value potential of AI across sectors and business functions is beginning to emerge in Asia.
12:05pm-12:45pm (40m) Becoming a data-centric company, Executive Briefing, Strata Business Summit
Executive Briefing: The five dysfunctions of a data engineering team
Jesse Anderson (Big Data Institute)
Early project success is predicated on management making sure a data engineering team is ready and has all of the skills needed. Jesse Anderson outlines five of the most common nontechnology reasons why data engineering teams fail.
1:45pm-2:25pm (40m) Becoming a data-centric company, Strata Business Summit
Executive Briefing: The data-driven growth engine
Jessica Chen Riolfi (TransferWise)
Data is essential to unlock growth opportunities, and successful companies use it in every decision. Jessica Chen Riolfi explains how to build an organization with decentralized, data-driven decision making that enables teams to focus on the products and features that matter and ultimately unlock exponential growth.
2:35pm-3:15pm (40m) Becoming a data-centric company, Executive Briefing, Strata Business Summit
Executive Briefing: How to structure, recruit, operationalize, and maintain your insights organization
Ricky Barron (InfoStrategy)
To many organizations, big data analytics is still a solution looking for a problem. Ricky Barron shares practical methods for getting the best out of your big data analytics capability and explains why establishing an "insights group" can improve the bottom line, drive performance, optimize processes, and create new data-driven products and solutions.
4:15pm-4:55pm (40m) Executive Briefing, Spark and beyond, Strata Business Summit
Executive Briefing: The business case for AI, Spark, and friends
John Akred (Silicon Valley Data Science)
AI is white-hot at the moment, but where can it really be used? Developers are usually the first to understand why some technologies cause more excitement than others. John Akred relates this insider knowledge, providing a tour through the hottest emerging data technologies of 2017 to explain why they’re exciting in terms of both new capabilities and the new economies they bring.
5:05pm-5:45pm (40m) Becoming a data-centric company, Executive Briefing, Strata Business Summit
Executive Briefing: Becoming a data-driven enterprise—A maturity model
Teresa Tung (Accenture Labs)
A data-driven enterprise maximizes the value of its data. But how do enterprises emerging from technology and organization silos get there? Teresa Tung explains how to create a data-driven enterprise maturity model that spans technology and business requirements and walks you through use cases that bring the model to life.
11:15am-11:55am (40m) Data science and advanced analytics, Machine Learning
Human-in-the-loop: a design pattern for managing teams that leverage ML
Paco Nathan (derwen.ai)
Human-in-the-loop is an approach which has been used for simulation, training, UX mockups, etc. A more recent design pattern is emerging for human-in-the-loop (HITL) as a way to manage teams working with machine learning (ML). A variant of semi-supervised learning called _active learning_ allows for mostly automated processes based on ML, where exceptions get referred to human experts.
12:05pm-12:45pm (40m) Design, UX, visualization, and VR, Strata Business Summit
Designing AI-based conversational UIs
Mohammed Abdoolcarim (Vahan)
For the first time, messaging apps have surpassed social networks in usage and growth. Mohammed Abdoolcarim shares best practices for designing for AI-based conversational UIs, such as those employed in messaging apps, drawn from work done at Apple, Google, and GoButler.
1:45pm-2:25pm (40m) Strata Business Summit
What executives and managers need to know about architecture and why
Jesse Anderson (Big Data Institute)
We have an explosion of new architectures. Are these new architectures because engineers love new things or is there a good business reason for these changes? In this talk, we will consider these new architectures and the actual business problems they solve. You may find out that your team is far less productive if you don’t move to these architectures.
2:35pm-3:15pm (40m) Data science and advanced analytics, Machine Learning
Talent Flow Behaviour Analytics: A Data Driven Approach to Human Capital Management
Philips PRASETYO (Living Analytics Research Centre, Singapore Management University), Ee-Peng Lim (Singapore Management University)
Analyzing talent flow behavior is important for the understanding of job preference and career progression of working individuals. When analyzed at the workforce population level, talent flow analytics helps to gain insights of talent flow and organization competition.
4:15pm-4:55pm (40m) Executive Briefing, Security and governance, Strata Business Summit
Privacy by design, not an afterthought: Best practices at LinkedIn
Shirshanka Das (LinkedIn), Tushar Shanbhag (LinkedIn)
LinkedIn houses the most valuable professional data in the world. Protecting the privacy of member data has always been paramount. Shirshanka Das and Tushar Shanbhag outline three foundational building blocks for scalable data management that can meet data compliance regulations: a central metadata system, an integrated data movement framework, and a unified data access layer.
5:05pm-5:45pm (40m) Executive Briefing, Security and governance, Strata Business Summit
GDPR: Getting your data ready for heavy, new EU privacy regulations
Mark Donsky (Okera), Steven Ross (Cloudera)
In May 2018, the General Data Protection Regulation (GDPR) goes into effect for firms doing business in the EU, but many companies aren't prepared for the strict regulation or fines for noncompliance (up to €20 million or 4% of global annual revenue). Steven Ross and Mark Donsky outline the capabilities your data environment needs to simplify compliance with GDPR and future regulations.
11:15am-11:55am (40m) Sponsored
Painless real-time scalable serverless data pipelines: What Google Cloud can do for you (sponsored by Google Cloud)
Felipe Hoffa (Google)
Stop worrying about infrastructure; focus on your data and insights. Felipe Hoffa explains how Google Cloud brings easy solutions to previously hard problems.
12:05pm-12:45pm (40m) Sponsored
Delivering a big data analytics API with 360-degree customer profile data from multiple industry data sources (sponsored by Kinetica)
Vira Shanty (Lippo Group)
Vira Shanty explains how the Lippo Group, one of the largest business conglomerates in Indonesia, is integrating data from multiple lines of business into a single big data analytic platform featuring an API layer with subsecond latency and how the company's mantra “deep and fast analytics” is opening new opportunities for improved customer engagement and new revenue streams.
8:50am-9:00am (10m)
Wednesday keynote welcome
Ben Lorica (O'Reilly), Doug Cutting (Cloudera), Alistair Croll (Solve For Interesting)
Program chairs Ben Lorica, Doug Cutting, and Alistair Croll welcome you to the first day of keynotes.
9:00am-9:15am (15m)
Computational challenges and opportunities of astronomical big data
Melanie Johnston-Hollitt (Victoria University of Wellington)
Keynote with Melanie Johnston-Hollitt
9:15am-9:30am (15m)
Siri: The journey to consolidation
Mick Hollison (Cloudera), Cesar Delgado (Apple)
Twenty years ago, a company implored us to “think different” about personal computers. Today, Apple continues to live and breathe that legacy. It’s evident in the machine learning and analytics architectures that power many of the company’s most innovative applications. Cesar Delgado joins Mick Hollison to discuss how Apple is using its big data stack and expertise to solve non-data problems.
9:30am-9:45am (15m) Strata Business Summit
Technology for humanity
Steve Leonard (SGInnovate)
Steve Leonard details how Singapore is bringing together ambitious and capable individuals and teams to imagine, start, build, and scale technology that can solve the world’s toughest challenges.
9:45am-9:55am (10m) Machine Learning
Responsible deployment of machine learning
Ben Lorica (O'Reilly)
Machine learning models are becoming increasingly widely used and deployed. Ben Lorica explains how to guard against flaws and failures in your machine learning deployments.
9:55am-10:00am (5m) Sponsored
Stop the fights; embrace data (sponsored by Google)
Felipe Hoffa (Google)
Organizations waste hours to endless discussions, and people lose sleep to internet debates. Can big data change this? Google Cloud is here to help. Felipe Hoffa explains that solid data-based conclusions are possible when stakeholders have easy access to analyze all relevant data.
10:00am-10:20am (20m)
Industrial machine learning
Joshua Bloom (GE Digital)
The ongoing digitization of the industrial-scale machines that power and enable human activity is itself a major global transformation. Joshua Bloom explains why the real revolution—in efficiencies and in improved and saved lives—will happen when machine learning automation and insights are properly coupled to the complex systems of industrial data.
10:20am-10:40am (20m)
Freedom or safety? Giving up rights to make our roads and cities safer and smarter
Bruno Fernandez-Ruiz (Nexar)
Keynote by Bruno Fernandez-Ruiz
8:00am-8:15am (15m)
Break: Coffee break sponsored by TigerGraph
8:15am-8:45am (30m)
Speed Networking
Ready, set, network! Meet fellow attendees who are looking to connect at Strata. We'll gather before Wednesday keynotes to host an informal speed networking event. Be sure to bring your business cards and have fun.
10:45am-11:15am (30m)
Break: Morning break sponsored by Google
12:45pm-1:45pm (1h)
Wednesday Topic Tables at Lunch (Located in room 324)
Looking to network with other attendees during lunch? Topic Table discussions help you connect with people in similar industries or interested in the same topics.
3:15pm-4:15pm (1h)
Break: Afternoon break
5:45pm-6:45pm (1h)
Sponsor Pavilion Reception
Need to unwind after a long day of sessions? Join us at the Sponsor Pavilion Reception and enjoy beverages and snacks with fellow Strata Data sponsors, attendees, and speakers.