XConf is our annual technology event created by technologists for technologists who care deeply about software and its impact on the world.
Join us for the third year in Australia brought to you live and online this year.
At this one-day conference, hear from technologists about their first-hand experience with emerging technologies and insights on technical trends.
With six exciting talks lined up, stay for the entire day or drop in for the sessions which interest you the most.
Chief Scientist,
ThoughtWorks
Martin is a renowned author, software consultant and international speaker, with two decades of experience helping corporations to evolve and utilise technology for mission-critical information systems. He was one of the authors of the Agile Software Manifesto and has written seven award-winning books on software development.
He is passionate about designing software systems to maximize the productivity of delivery teams, allowing them to respond to the changing needs of customers, fast. He enjoys collaborating with his fellow ThoughtWorkers, discovering the patterns of good software design, and the processes that underpin successful software engineering, then communicating these concepts more broadly to benefit the wider world.
Director of Emerging Technologies, ThoughtWorks, North America
Zhamak Dehghani is a Director of Emerging Technologies with a focus on distributed systems architecture and digital platform strategy at Enterprise. She is a member of the ThoughtWorks Technology Advisory Board and contributes to the creation of the ThoughtWorks Technology Radar.
Zhamak has worked as a software engineer and architect for 20 years in the areas of distributed computing communications, as well as embedded device technologies and she has contributed to multiple patents on embedded mobile sensing devices.
Director of Technology,
ThoughtWorks, AsiaPac
As Director of Technology for ThoughtWorks Asia Pac region, Scott divides his time between customer-facing consulting work and internal technical leadership. As a leader in ThoughtWorks, he's responsible for our technical brand in Australia, ensuring that we're delivering only the highest technical quality. He helps foster and share innovation on our delivery projects and develops leadership skills in our best and brightest technologists.
Scott works closely with ThoughtWorks CTO Dr Rebecca Parsons, as well as his colleagues on ThoughtWorks' Technical Advisory Board. One product of our global collaboration is the quarterly ThoughtWorks Technology Radar.
Senior Data & ML Engineer
Senior Data Engineer
Data Consultant
Senior Developer & Consultant
Head of Design, Operations
Senior Data Consultant
Lead Consultant
Principal Consultant
Social Scientist, Australia
As a Social Scientist, Nigel equips organisations with the tools and thinking that can accelerate them to become a Modern Digital Business – seamlessly managing online to offline in a post-customer, more human-centric world.
Nigel Dalton
Martin Fowler & Scott Shaw
In recent years we’ve seen the types of applications and the architectural environments in which we work become more heavily reliant on data in myriad forms. The data itself goes far beyond simple persistence stores to include streaming operational and business events, massive data lakes, legacy silos and distributed networks of democratised data products. This has changed the tools and platforms we use to build applications while at the same time, reinforcing timeless engineering practices. We’ll discuss these concepts and more and take a look at where things might be going in years to come.
Ann Mwangi & Sarah Taraporewalla
Event-Driven Architecture (EDA) is gaining widespread popularity as organisations look for ways to simplify the coordination of their microservices. Despite the growing interest of Kafka, and the early success of EDA, there is still a significant distance between understanding the concepts and publishing your first event in production. Hear our speakers share their own journeys towards publishing their first event to production including the common challenges they faced and the different techniques they used. Discover the considerations you need to publish your first event.
Diana Adorno & Karen Davis
Machine Learning has opened up enormous possibilities for organisations to do things better, faster and more insightfully than ever before. However, in the rush to automate, the people around these machines are often forgotten, overlooked or the impact on them dismissed. People need to be at the centre of any design, so how do we balance the benefits of machine learning with empathy? During this talk, hear stories, principles and techniques that will ensure people are considered and we end up with better outcomes all round.
Louise Williamson: yoga & guided meditation
Zhamak Dehghani
This talk will cover an introduction to data mesh and the motivations behind it - the failure modes of past paradigms of big data management. Zhamak will compare and contrast data mesh to existing big data management approaches, and introduce the technical components underpinning the architecture.
Jessie Wang & Brad Nguyen
The general supervised learning problem starts with a labelled dataset. However, it’s all too common to have an additional large collection of unlabelled data. Self-supervision techniques can be a great way to make use of this unlabelled data to boost performance without requiring too much manual input. Walk away with fundamental techniques that can be used to provide weakly labelled data or to pre-train representations (embeddings) for further downstream machine learning tasks. We’ll also present examples and discuss techniques including active learning, few shot training and representation learning as well as concerns relating to operationalising ML models, scalability and ML operations when applying these techniques in practice.
Harmeet Sokhi & Kiru Samapathy
Most organisations implementing Machine Learning (ML) want to bring data science to mainstream problem solving and scale. However, most are still trying to figure out how to best build end-to-end robust machine learning training and prediction pipelines. In reality, organisations often start with great ideas, throw together test data, build a ML model as a proof of concept, verify the business benefits and instantly decide to productionise the model. However, this often leads to challenges during production. In this talk, learn how to prioritise infrastructure decisions for incremental delivery, how to design for longevity and robustness, and the pros and cons of various operating models found in the wild.
Speakers
