Mary Grygleski

Developer Advocate, IBM

Mary is currently a Java Developer Advocate at IBM, specializing in Reactive Java systems. She started working as a software engineer with C and Unix, then got into Java, Open Source, and web development in the new Millennium, and now she is venturing into Reactive, Mobile, and the DevOps space. In her previous incarnations, she worked for several technology product companies in the Route 128 Boston Technology Corridor as well the San Francisco Bay Area. She now resides in the Greater Chicago area, and is an Executive Board member and the Director of Meetings for the Chicago Java Users Group (CJUG). She is also an active co-organizers for the Big Data Developers in Chicago, Chicago Cloud, and IBM Cloud Chicago meetup groups.

Mary continues to be amazed by how software innovations can dramatically transform our lives. Despite the many challenges in an ever-evolving technical world, she gets energized by the constant change and believes that she has uncovered the pathway to stay young. She can’t wait to see what the next tech wave will be like.

 

Talks on Wurreka:

We have been hearing a lot about the benefits of using the reactive approach to solving concurrency problems in distributed systems. While reactive programming refers to the implementation techniques being used on the coding level, on the systems deployment and runtime level, we can leverage on a robust yet very flexible and lightweight framework such as Vert.x to deliver.

In this session, we will first learn about what the missions of a reactive system are, which, among many things, include handling multiple concurrent data stream flows and being able to control back pressure as well as managing errors in an elegant manner. We will also discuss the special polyglot nature of Vert.x and its use of the Vertical model. Live coding will accompany this session to illustrate how to program a simple use case using multiple JVM languages such as Java and Kotlin, and then we will build and dockerize it to be deployed as a serverless container to the Knative cluster in a delightful manner.

With the advances in multicore hardware and virtualization technologies, and the demand for highly responsive, resilient, and elastic systems and increasingly sophisticated applications, an array of reactive data stream processing libraries have been born to address the needs. Reactive Streams is an initiative to provide a standard for asynchronous stream processing with non-blocking back pressure. This encompasses efforts aimed at runtime environments that include JVM and Javascript, as well as network protocols. So how do the various library implementations of Reactive Streams, such as Spring Reactor, Reactive Extension (Rx)'s Observables, and RSocket, stack up against each other?

This presentation will go into some details on how streams leverage on the underlying multicore processor to achieve parallelism. It will then explain the push vs the pull streaming model. It will then use a simple use case with code examples to illustrate the different API usages, as well as runtime processing analysis between a few popular Java implementations of Reactive Streams.

Today the adoption of Machine Learning is enormous. We use it almost everywhere: on clusters, on our phones, on hand watches and even on the teapots. But, who knows how challenging could be the development of the continuously improved (or even real-time) machine learning. Servers' capacity, stability, performance, along with a proper ML algorithm selection and many other properties must be taken into account. In this presentation, we will be demonstrating bots education for multiplayer Pacman game. We will show how elegant could be the development of such a problem with Kafka, Akka, RSocket and Reinforcement/Q Machine Learning

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