Introduction from chair - Menti ice breaker - Why are we here
A menti ice breaker session
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.
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
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
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?
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.
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
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.
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?
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
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.
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
Financial advisors have left the building
- Research overview on The Rise of the Roboadvisors
- How did we get here?
- Where are we going?
Machine learning and security
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.