Rebecca Whitworth | SpeakerHub

Personal Details

Bio

Rebecca Whitworth is an associate manager at Red Hat. Here she is part of a new initiative researching into trustyAI. She completed a PhD at Newcastle University, in which she developed a platform for scalable, geospatial and temporal analysis of Twitter data. After this she moved to a small startup company as a Java developer creating solutions to improve performance for a CV analyser. She has a keen interest in ML and AI and continues to research this within the BA team.

Current position (1)

Associate Manager

Red Hat

Achievements (1)

Rising Star Nomination for Women in Tech

Women in tech having a rising star award and I was nominated for this.

Degrees (1)

PhD Computing Science

Newcastle University

2011 to 2015

Presentations

Presentations (4)

Apache Spark from notebook to cloud native application

Spark+AI Summit • San Francisco, CA • June 2018

Data engineering teams love Apache Spark because it’s powerful and easy to manage, but managing a shared resource for experimental analyses and queries is very different from developing production applications in contemporary cloud environments: the gap between understanding Spark and being able to deploy and manage it in production can be vast.

This session will cover a developer’s journey learning Spark and using it to develop a containerized, cloud native application with analysis and visualization components. More specifically, these topics will be covered:

Exploratory analysis in a Jupyter notebook running against an ephemeral Spark cluster

Using PySpark for loading and analyzing data from external data sources like PostgreSQL

Transforming your notebook into a cloud-native application deploying your application in containers on Kubernetes

PySpark API functionality that you didn’t know you needed.

Intelligent applications on OpenShift from prototype to production

Today’s users demand tailored, dynamic, and constantly refined experiences. They expect intelligent applications that will learn from data and improve with longevity and popularity. Application intelligence takes many forms, including anomaly and fraud detection, product recommendations, natural-language understanding, even speech and image recognition. All of these capabilities will need to be put into production and managed alongside conventional application components.

In this session, you’ll:

See how developers can integrate intelligent features into their products without involving data scientists or machine learning engineers.

Understand how application intelligence is created and refined by cross-functional teams and how using OpenShift can accelerate this process.

Watch us demonstrate architectures for creating, sharing, deploying, and reusing intelligent application components as containerized microservices.

Leave inspired to take your applications to the next level

Application intelligence: Bridging the gap between human expertise and machine learning

As industry media continues to drive the popularity of the term “artificial intelligence,” it leaves many unanswered questions. How will software teams deal with the increased demand to “add more AI” to their projects? Where do your domain experts add their experiences to the algorithms? How can business processes be improved through machine learning?

Rebecca Simmonds and Michael McCune offer answers to all these questions. You’ll see how to combine the worlds of data science and business process management to form processing engines that deliver reduced overhead and improved automation and learn how distributed machine learning frameworks fit together naturally with business rules management systems. Join in to discover how to enhance your rules-based processes with machine learning-inspired analysis.

Machine Learning: From buzzword to reality with Java

Commercially, machine learning and artificial intelligence are a popular trend, but as a Java developer (not a data scientist) how do we incorporate these techniques intelligently into our applications? Machine learning (ML) techniques can support the development of intelligent applications for facial or speech recognition, pattern matching, fraud detection, driverless cars and many more. However, how can you decipher whether these methods are just popular buzzwords or a profitable venture for your companies applications? This session will cover my journey as a Java developer learning how to use ML techniques intuitively and produce my own intelligent application. We will explore how to prototype your own ML algorithm implementations and then how this can be transformed into a cloud native application and deployed with Kubernetes. The session will delve into designing and implementing microservice architectures and present how to leverage their advantages.