Thursday, April 11, 2019

Red Hat Summit 2019: Emerging Technology Labs Roadmap


Red Hat Summit 2019 is rocking Boston, MA from May 7-9th in the Boston Convention and Exhibition Center.

Everything you need to know about the current state of open source enterprise ready software can be found at this event. From customers talking about their experiences leveraging open source in their solutions, to the creators of open source technologies you're using, and all the way down to hands-on lab experiences on these technologies.

This hands-on appeal is what this series of articles is about. It's interesting to take a tour, so starting with this article let's examine a series of instructor-led labs based on a specific theme.

This week it's a roadmap to emerging technology lab content.

The following labs can be found in the session catalog online, by searching on title or filtering on instructor-led labs and emerging technology.

Analytics and machine learning with Red Hat infrastructure

As data is exponentially growing in organizations, there is an increasing need to consolidate silos of information into a single source of truth, or ‘Data Lake’ to feed hungry Analytics and Machine Learning Engines that can gather insight at scale.

In this session, we'll detail how to architect data infrastructure services using Red Hat OpenShift, Red Hat Ceph Storage, and doing analytics with Spark and TensorFlow. In the hands-on segment of the lab, we'll deploy Open Data Hub and use Jupyter notebooks to walk through interacting with data sets using the S3A filesystem client and using Spark schema detection and SparkSQL to query data. We'll then look at how to use TensorFlow to create a model to classify data, how to integrate TensorFlow models and Spark, and at how to serve that data using Red Hat OpenShift.

Speakers: Kyle Bader, Sean Pryor, Sherard Griffin


Machine learning workflows for application developers

The capabilities of intelligent applications often seem like magic to users, but the machine learning and artificial intelligence techniques that provide these features are more accessible than you might think. Developing intelligent features doesn’t require esoteric math or high-performance hardware, but it does require you to adapt your existing engineering practice to build and manage predictive models in addition to conventional software artifacts.

This hands-on lab will introduce machine learning workflows and show you how to integrate them into the application development work you’re already doing, focusing on the habits and processes that will help you to get meaningful results from application intelligence. During this lab, Red Hat machine learning experts will help you:

  • Build intelligent application functionality from the ground up, by training, evaluating, and deploying predictive models.
  • Incorporate machine learning into your general software development discipline.
  • See how to apply techniques like continuous integration, iterative development, and monitoring while building intelligent features for your apps.
  • Learn how the development platforms you love, like Red Hat Middleware and Red Hat OpenShift Container Platform, support every phase of intelligent application development.
Speakers: Sophie Watson, William Benton, Michael McCune

Next-gen technologies at scale: Building solutions to manage tomorrow’s workloads

For emerging technologies like Internet of Things (IoT), virtual reality (VR), and 5G, a large amount of data is generated outside the datacenter—for example, a refinery can generate 1TB data per day. A lot of this data is redundant, yet an event for critical equipment would require a near real-time response. This requires a large amount of data processing at the edge as well as reducing the data volume sent to datacenter or cloud.

In this lab, you'll learn how to use a combination of Red Hat technologies, such as Red Hat Decision Manager, Red Hat AMQ streams, and Red hat OpenShift Container Platform to build integrated solutions to meet most demanding workloads. In particular, we'll cover using the features of AMQ streams—such as stream processing, metering, and event sourcing—to build solutions that scale for complex environments. Patterns used in this lab are designed to be extensible, so that you'll understand how to implement your own adaptive solutions afterwards.

Speakers: Hugo Guerrero, Sam Rang, Andrew Block, Christina WeiMei Lin, Ishu Verma


Stay tuned for more articles with insights into other themes that might interest you enough to register for one of these instructor-led labs at Red Hat Summit 2019.

Looking forward to seeing you there!