In partnership with:
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Getting the organisation AI-ready
- Wednesday, 30th March 2022
- Registration 12:25pm to 2pm
- Location: Virtual
Ignoring artificial intelligence (AI) is no longer an option. IT leaders have to understand the opportunities and limitations of AI and decide where and how AI makes sense and creates business value. Many organisations have already launched AI programs or proof of concept projects, but poorly implemented AI can lead to poor customer experience. The organisation has to be AI-ready. AI requires a data infrastructure, processes, policies and data science talent embedded in IT or functional departments ready to support the applications.
Join this Digital Boardroom and explore with us
- why ethics should be part of the discussion getting started with AI
- how extending DevOps to MLOps can unlock your AI capabilities
- what an AI-ready data infrastructure looks like
- what companies are already doing with AI and machine learning (ML)
Our Keynote Speakers
Nigel Willson, Founding Partner at awaken AI
Explainable AI and how to start implementing it
Artificial Intelligence is everywhere in our home and work lives. It doesn’t matter whether we design it, sell it or just use it, we shouldn’t underestimate the impact of this powerful technology and must understand how its decisions and recommendations are reached. Explainable Artificial Intelligence is easy to describe and hard to achieve. Hear about what Explainable AI is, the impact it could have on your organisation and how to start thinking and planning for what will likely become legislation in the not to distant future.
Juan José López Murphy, Global Head of Data Science and AI at Globant
Embracing MLOps to unlock the AI capabilities of your organisation
The only way AI can make an impact in organisations is through sustained real world use, “in production”, through all changes in business context, input data, processes, infrastructure, teams and everything else. To handle, automate and simplify the complexity of embedding AI in the organisation a new discipline with associated platforms is emerging called Machine Learning Operations or MLOps, an evolution of DevOps. MLOps are best practices for AI. What are the aspects that we need to pay attention to when pushing AI into the real world? What are the typical issues tackled by MLOps and how are they solved? How can we get an idea of how the moving parts are tied together and the value that MLOps bring? In essence, what do we need to know to get a working sense of MLOps?
Abed Ajraou, Head of Data & Insight, E.ON Next
Avoiding the pitfalls of data science projects
How to put data science into practice inside our organisation. Abed Ajraou will illustrate the main mistakes to avoid and how to build great teams to make data science projects work. Abed has over 22 years in Data Analytics/Business Intelligence backed with a Master’s Degree in Computer Science. He was Data Director and AI Lead at Shell Energy and the CDO of Peculium/AIEVE, an AI product on top of the blockchain/crypto currency. Abed is currently Head of Data & Insights at E.ON Next.
Event Chair: Aline Hayes, Director of Technical Transformation, EMEA at Experian
Aline has worked in a range of sectors, with senior leadership roles in Further and Higher Education, Local Government, Banking and Information Services, including over ten years as a CIO and five years as a Non-Executive Director. She is currently leading major business transformation across EMEA, working with a multi-disciplinary team across eight time zones. Professionally, Aline is interested in data, software engineering, AI/ML and delivering better business outcomes through technology. She is currently a member of the Computing Employer Advisory Board at Sheffield Hallam University and regularly mentors people at different points in their careers.
