Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

MapR Technologies
Come see us in the Expo Hall
booth #1009

MapR Technologies

MapR Technologies, a pioneer in delivering one platform for all data across every cloud, enables companies to harness the power of all of their data with the MapR Converged Data Platform. With the MapR Platform, companies can do analytics and applications together as data happens to create intelligent, next-generation applications to outperform the competition. Global 2000 enterprises are using the MapR Platform to help them solve their most complex data challenges. Amazon, Cisco, Google, Microsoft, SAP and other leading businesses are all part of the MapR ecosystem.


5:10pm–5:50pm Wednesday, 03/07/2018
Ted Dunning (MapR)
Ted Dunning offers an overview of the rendezvous architecture, developed to be the "continuous integration" system for machine learning models. It allows always-hot zero latency rollout and rollback of new models and supports extensive metrics and diagnostics so models can be compared as they process production data. It can even hot-swap the framework itself with no downtime.
11:00am–11:40am Wednesday, 03/07/2018
Tom Fisher (MapR Technologies)
The monolithic cloud is dying. Delivering capabilities across multiple clouds and, simultaneously, transitioning to next-generation platforms and applications is the challenge today. Tom Fisher explores technological approaches and solutions that make this possible while delivering data-driven applications and operations.
1:50pm–2:30pm Wednesday, 03/07/2018
Ted Dunning (MapR)
Getting value from data at large scale and on a variety of time scales is hard. True, it's not as hard as it used to be, but you still don’t win by default. Ted Dunning explains why it takes good design, the right technology, and a pragmatic approach to succeed.
2:40pm–3:20pm Wednesday, 03/07/2018
Ellen Friedman (MapR Technologies)
DataOps—a culture and practice for building data-intensive applications, including machine learning pipelines—expands DevOps philosophy to include data-heavy roles such as data engineering and data science. DataOps is based on cross-functional collaboration resulting in fast time to value and an agile workflow. Ellen Friedman offers an overview of DataOps and explains how to implement it.
11:50am–12:30pm Thursday, 03/08/2018
dong meng (MapR)
Deep learning model performance relies on underlying data. Dong Meng offers an overview of a converged data platform that serves as a data infrastructure, providing a distributed filesystem, key-value storage and streams, and Kubernetes as orchestration layer to manage containers to train and deploy deep learning models using GPU clusters.
1:50pm–2:30pm Thursday, 03/08/2018
Ted Dunning (MapR)
If you're interested in machine learning and the logistical aspects of supporting it in production, come talk to Ted. He'll also discuss: data platforms, streaming architecture, Kubernetes, containers, and rendezvous architecture.
9:00am–9:10am Thursday, 03/08/2018
Anoop Dawar (MapR Technologies)
We are inundated with ideas and technology news in today’s data-rich but attention-deficit economy. In this environment, competitive advantage comes not from what is abundant (i.e., data) but from what is scarce—the ability to deploy insights in real time. Anoop Dawar explains how your peers are succeeding in shrinking the insight-to-action cycle and achieving great results.
11:00am–11:40am Thursday, 03/08/2018
Jim Scott (NVIDIA)
The value of data is not strictly a function of its size but rather is in the value that can be extracted from it. Jim Scott explains how to identify the right data to leverage to monitor the pulse of fast changing business environments, the best way to integrate analytics into your business processes, and the importance of cross-application data flows.