Skip to navigation Skip to main content

Day 1

Registration and breakfast

Introduction from chair - Menti ice breaker - Why are we here

A menti ice breaker session

Robert Gray Chief Technology Officer Top Flight

Untapping our potential

- We believe data should be a source for good, our data is a window into our lives and a catalyst for positive change, for untapping our potential

- At Untapped we don’t design and build systems that replace or compete with human intelligence, instead we augment it . The real value in AI is its potential to augment people, not to replace them. Machine learning helps us to annihilate our weaknesses. We don’t have consistent attention spans.

- Untapping our potential is about working on our strengths, not just weaknesses, understanding our culture and the tensions between us and our environment.

Brendan O'Hara Co-Founder Untapped

Journey of a Data Science Project

- Delivery methodology of a data science project
- Key attributes of a machine learning product
- The journey of a data science project, from the scoping to the delivery

Michele Usuelli Lead Data Scientist Microsoft

True value out of data

- Foundations of data and data processing
- Organizational challenges in data and analytics
- An application of ML for the hospitality industry

Max Cottica CIO Staycity Group

Becoming a data-driven business

Our panellists will discuss how your business can be more data driven.
Answering questions like what does a future-proof data collection strategy look like?

Michele Usuelli Lead Data Scientist Microsoft
Max Cottica CIO Staycity Group
Brendan O'Hara Co-Founder Untapped

Morning coffee

How to build a Machine Learning driven organisation

- How to set up ML team?
- The Graal: Business - Tech communication.
- New processes, culture and tech management.
- ML trends in 2019.

Hubert Misztela Machine Learning Expert, Novartis

Artificial intelligence the big picture

- Macro view on AI, the big picture

- What AI means for potential business growth, examples & use cases

- Lessons learned through the definition of the Artificial Intelligence strategy

Natalia De Miguel Former Artificial Intelligence Strategy Director GSMA

Machine learning: It’s the data, stupid

- A brief history of the Relational DB and what we can learn from its development in terms of ML.
- Data as an enabler for ML: What does great look like?
- Challenges and possible solutions.

Eoin Delahunty Data Product Owner - Machine Learning & Analytics Paddy Power Betfair

ML methods, doubts and the future - Q&A

- How do you pick the right one to deliver the greatest impact for your business, as applied over your data?

- What are the best practices along the way?

- How do you avoid the most treacherous pitfalls?

Ajay Soni Senior Software Architect & Engineer RBS
Hubert Misztela Machine Learning Expert, Novartis
Eoin Delahunty Data Product Owner - Machine Learning & Analytics Paddy Power Betfair

Lunch

How to bring ML decision making in real-time in production

- Intelligent real time machine learning applications are a game changer in any industry.

- In the transactional fraud detection industry, we have to make approve or decline decisions within 100 milliseconds.

- How can you manage machine learning model artefacts, data & feature engineering, model & rule execution and verification, continuous deployment, monitoring & analytics and more

Ajay Soni Senior Software Architect & Engineer RBS

Tips and tricks learnt from the health and life sciences

Tips and lessons learnt from implementing machine learning. I will be talking about my research in various machine learning projects in the health and life sciences domain.

Paul Walsh Director Sigma Machine Learning Research Group

The coolest project I have done in the last 25 years

- Case study of “Optimizing Computer Aided Design of Oil Rig Support (Jackets)”

- The experience of morphing from an R&D on analytics to a software Factory

- Discussion of a framework for managing a data science project

Eli Kling Director Cognizant

Financial advisors have left the building

- Research overview on The Rise of the Roboadvisors
- How did we get here?
- Where are we going?

Stephen Dooley CEO UCC Student Managed Fund

Afternoon coffee

Machine learning and security

Aidan Connolly CEO Idiro Analytics

Is ML Breaking Up With Data Scientists?

- There is a paradigm shift around the corner with how Machine Learning will be used by Businesses.
- Self-served analytics will power Business Units from within rather than executed and deployed by Data Scientists.
- Data Scientists will need to ramp up their business skills if they want to stay relevant.

Maciek Wasiak CEO Xpanse

Closing remarks from Chair

Robert Gray Chief Technology Officer Top Flight

Networking drinks