Lead Architect - Data, AI & IoT @ Microsoft
Senior Data Science Manager @ Betfair
Analytics Director @ IAG
Chief Data Scientist @ AlphaZetta
Machine Learning Engineer @ Wesfarmers
Analytics Integrator @ Doc Savage Solutions
Co-Founder @ AI On Spectrum
Co-Founder @ AI On Spectrum
Co-Founder @ AI On Spectrum
General Manager @ AlphaZetta
You can learn a lot about data science through text books, papers and blogs. However, every data scientist will tell you they have learned invaluable lessons through their every day work that isn't necessarily documented anywhere. This talk will take you through all things you can learn about being a better coder, being a better modeller and also how incredibly powerful it is to be both the technical person and also the key stakeholder.
Kaushik Lakshman - Senior Data Science Manager @ Betfair
Kaushik Lakshman is the senior data science manager at Betfair. He leads a team of data scientists to trawl through incredibly rich and extensive betting data to build data science products that make Betfair a better customer experience. Kaushik is passionate about the data science community and the power of learning from each other.
Linear Regression is a foundational modelling technique yet it is often neglected in favour of "more advanced" algorithms. However, one may find that like fine wine it has improved with age due to a number of theoretical developments, simplifications and practical aspects. In addition to considering the definition and calculation of linear regression models, this talk will consider many of the auxiliary tools that accompany these models. An example problem will be worked to illustrate the concepts.
Craig Savage - Analytics Integrator @ Doc Savage Solutions
Dr Craig Savage has worked in a number of technical fields, including rocket science and bringing sight to the blind. He has experience in Credit Risk at NAB and ANZ, and is currently an independent consultant through is his firm, Doc Savage Solutions. His professional interests include data analysis, data visualisation and, more importantly, driving evidence-based decisions and actions.
Machine learning models have previously been built with no direct path to production, on stale and isolated copies of production data. This process was not repeatable, explainable or scalable and often introduced business and security risk. Modern enterprises are now adopting a DevOps engineering culture. Data science teams can now leverage DevOps practices to integrate with the business and existing applications teams. The path to production become built into the process. Ananth will show you how to bring data science into the age of modern DevOps.
Ananth Prakash - Lead Architect - Data, AI & IoT @ Microsoft
Ananth has a successful track record in delivery of strategic projects, products and platforms in the Data & AI domain for 16 years across North America, Australia and Asia. Some of his roles include leading product engineering groups at Oracle, Managing Consultant at IBM and Senior Technical Architect - Data & AI at Infosys. As a Lead Architect at Microsoft, Ananth helps enterprise customers architect & deploy strategic solutions on Azure to accelerate their business transformation journey.
The popularity and ubiquity of data science, data analytics, AI and the trend towards digital transformation have led to massive, repeated failures in many businesses. Despite billions spent, hundreds of Ph.D.s hired, and much boasting in conference presentations, many enterprises are still struggling to leverage the value of these new technologies. The missing ingredient is the literacy of the rest of the organisation, particularly senior management.
This presentation will describe this new literacy: “data literacy”, the analogy with computer literacy, and reasons why this skillet will soon be as essential to all professionals as data literacy is today. It will address issues of automation, the advent of decision making as the key managerial activity and the resulting democratisation of AI and analytics, however still maintaining a class of data science and analytics experts. The presentation will address issues of mindset, as well as skillset, and the ways in which management engagement with data analytics must change to leverage its value.
Eugene Dubossarsky - Chief Data Scientist @ AlphaZetta
Dr Eugene Dubossarsky is a leader in the analytics field in Australia, with over 20 years’ commercial data science experience. He is a the Managing Partner of the AlphaZetta Global Analytics Training Academy as well as AlphaZetta's Chief Data Scientist. Eugene has worked as a data scientist, software developer, entrepreneur, trainer, consultant, financial trader and speculative sports punter, applying his data science and analytics skills in all these fields. He is also the founder of the Australia–New Zealand Data Analytics Network, with over 19,000 members, and the head of the Data Science Sydney group (6,000+ members), and Big Data Analytics Sydney (7000+ members). Eugene is the Chief Scientist of reask, a global climate and catastrophe modelling company. He is regularly invited as a conference presenter, consultant and advisor, and appears in print and on television to discuss data science and analytics, and advisory board member for listed companies, advising on AI and Data Science. Eugene also applies data science in an entrepreneurial setting, to financial trading and online startups, and is the creator of ggraptR, an interactive visualisation package.
All models are wrong, but some are useful. In most organisations, only a handful of useful models ever make it to production.
In this session, we will look at how MLOps helps to rapidly bring models to production. The talk will also cover best practices for automated deployment and monitoring of productionised models, common tools and techniques used in the MLOps space, and how to measure MLOps maturity within an organisation.
Pasan Karunaratne - Machine Learning Engineer @ Wesfarmers & Senior Consultant @ Servian
Pasan leads the Artificial Intelligence Community of Practice at Servian - a cloud, data, and analytics professional services organisation. He holds a PhD in machine learning from the University of Melbourne, and has experience ranging from pure machine learning modelling to architecting production-ready machine learning platforms on cloud infrastructure. Pasan has broad industry experience, having worked across the financial services (NAB), telecommunications (Telstra), media services (Foxtel), and retail (Wesfarmers) sectors.
We aim to improve the lives of children on the Autism Spectrum. We will present EMODO - our accessible solution that provides better educational outcomes and better support for children on the Autism Spectrum.
The AI On Spectrum Team
Gillian Ong - Co-Founder & Community Engagement @ AI On Spectrum
Originally from a Media & Communications background, Gill decided to pursue her passion in psychology and early childhood education & care by putting her degree to good use as the chief evangelist. She's also an avid neurodiversity advocate and believes in inspiring others to make the world a more inclusive place.
Anton Polevoy - Co-Founder & Software Engineer @ AI On Spectrum
Anton is a game developer graduate who has experience in developing educational games for early readers and writers and educational 3D strategy in the Unity game engine. At AI On Spectrum, Anton works on the backend, the ML and the web development.
Kateryna Tsysarenko - Co-Founder & Software Engineer @ AI On Spectrum
Kateryna is an iOS mobile app developer who has worked on different kinds of iOS apps, currently working for Drawboard. She also has experience in project management. At AI On Spectrum, Kat's current focus and passions are UX and UI.