Day 1 - 25 April 2019
Data Analytics for AI and IoT: Chair’s welcome and opening remarks
Ensuring Your AI Projects are Successful
This talk will be given by Ian Tinney, SVP Technical Services and Cloud Operations at Gemini Data
Keynote: Solving customer problems with the power of AI
Digital demands a mind shift: from developing technologies in a vacuum to solving customer problems with relevant technology. In that context, what is the business value of AI? And how can industry incumbents such as Schneider Electric lead in the age of AI? AI is the framework behind the ability of any company to develop new digital models. Imagine a long-time specialized machine builder, for example, now being able to offer a digital service such as “uptime as a service.” AI enables this type of digital transformation. Take a deeper look: machine learning can overhaul the way Oil & Gas companies manage remote assets such as onshore oil pumps. To drive value, AI must be complemented by domain expertise for better data context and better models.
Hear from Schneider Electric Chief Technology Officer Ibrahim Gokcen how AI is solving customer problems – becoming an applicable digital advancement that is paving the way for disruptive companies to thrive in the digital economy.
Filippo F.G. Della Casa
Head of Analytics, Chief Innovation Officer Department
Leithà – Unipol Group
11:40AM - Day 1
5.5 years of Data Science in a Large Corporate: lessons learnt, through use cases
To follow soon…
Head of Data Science
International Adviser / Former CIO
Sightline Innovations / Digital Greenwich
12:30PM - Day 2
Venturing & Incubation Lead
Panel: IoT and AI data analytics for intelligent decision making
- Identifying target-rich, high-value data that can be used to generate business intelligence
- Using cloud analytics platforms to derive value from IoT data
- Discussing the barriers to widespread IoT/ AI /Big Data value delivery and how these might be overcome.
- Real time data analytics in practice – examples of how IoT / AI data is creating business efficiency and revolutionising working practices
DevOps & Tech Evangelist
12:40PM - Day 1
Adopt dataops practices & build a datahub to industrialize data and AI projects
Data is the new oil, many say. But to get value of all this data sleeping in your databases and datalakes, you need to adopt industrial processes and build up a refinery. In this session, you will first understand the key principles of DataOps, a methodology which streamlines and accelerate data and AI projects. On the technical side, you’ll also learn what a datahub is, and how to build one leveraging on the Kubernetes ecosystem (using Helm and Argo for instance), as well as other key technologies generally used in data projects : Apache Spark, Kafka… And finally, what this has to do with AI ? Join this session, and you’ll understand the link.
Former Head of Performance Reporting and Analytics and the COO – Performance and Assurance
Tideway Project London.
02:20PM - Day 1
Data warehouse, Business Intelligence and Analytics on the Tideway Project
The presentation will cover introduction on the project Tideway project, an over £3bn infrastructure project to complement and modernise the London sewage system. Focus will be inception, needs, implemented Data and information solutions and lessons learnt.
Big Data Architect
Mr & Mrs Smith
02:50PM - Day 1
Principal Data Scientist
02:50PM - Day 1
Data Science Expert
02:50PM - Day 1
Lead Data Scientist
Marks and Spencer
02:50PM - Day 1
Panel: Big Data – Creating Intelligent Data Models
- The increased need for big data analytics to drive AI & Machine learning
- How to successfully unlock unstructured data & transform into learnable features
- The advancement of self-service big data tools & its benefit for your organisation
Institute for Ethical AI & Machine Learning
03:30PM - Day 1
Industry-ready data & machine learning pipelines
This talk will provide a practical deep dive on how to build industry-ready machine learning and data pipelines in Python. I will cover a hands-on case study that will build from the basics of Airflow, and show how it is possible to build scalable and distributed machine learning data pipelines using a distributed architecture with a producer- consumer backend using Celery. I will provide insights on some of the key learnings I have obtained throughout my career building machine learning systems, as well as caveats and best practices deploying scalable data pipelines systems in production environments.
Lead Product Consultant Omni-channel Analytics
04:00PM - Day 1
Making the Most of the Power of Payment Data
To follow soon ….
MeetUp: Data Science Initiative – Automated ML and Generative Adversarial Networks
The Data Science Initiative (DSI) is an initiative that aims to create an environment where anyone with a passion for Data Science can learn this field at an introductory, intermediate, and/or advanced level. The initiative’s main objective is to equip students with the right skills to enter the growing and expanding market of data science.
17:00 – 17:20 – Automated Machine Learning – An Overview by Fatos Ismali
In recent years we have seen an upsurge of Machine Learning techniques being applied to almost all kinds of problems types: classification, regression, clustering, market basket analysis, and many more. This has become a challenge for the typical Data Scientist who is usually faced with a search space of algorithms and configurations that is usually too time-consuming and practically inefficient to go through. In this talk, we introduce the topic of Automated Machine Learning and more specifically automated feature engineering, model selection, hyper-parameter tuning, and neural architecture search.
17:20 – 17:25 – Q&A
17:25 – 17:55 – Overview of Generative Modelling, GANs & adversarial attacks by Jakub Langr
Until recently, generative modeling of any kind has had limited success. But now that Generative Adversarial Networks (GANs) have recently reached few tremendous milestones (and truly exponential growth in the interest in this technology), we are now closer to a general purpose framework for generating new data.
Now GANs can achieve a variety of applications such as synthesizing full-HD synthetic faces, to semi-supervised learning as well as defending and mastering adversarial examples, we can discuss them in this talk. In this talk, we will start with the basics of generative models, but eventually, explore the state of the art in generating full HD images as presented in https://arxiv.org/abs/1710.10196 and dive into adversarial attacks and why this matters to all computer vision algorithms.
17:55 – 18:00 – Q&A