Day 1 - 25 April 2019

Data Analytics for AI & IoT

  • Big Data
  • Edge processing
  • Data analytics for sport
  • Predictive analytics
  • Data architecture

09:40AM

Paige Leuschner

Research Analyst for Residential Energy Innovations

Navigant

Associated Talks:

09:40AM - Day 1

View Data Analytics for AI and IoT: Chair’s Welcome and Opening Remarks

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Data Analytics for AI and IoT: Chair’s Welcome and Opening Remarks

. Paige Leuschner, Research Analyst for Residential Energy Innovations , Navigant
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10:00AM

Edge Processing for Data Analytics and Training AI Algorithms

  • How the huge influx of data will require fit-for-purpose architecture. What is the distance from the edge to your device and how to consider this during the creation of your IOT / AI architecture?
  • Discussing how IoT / AI architectures need to be put in place to ensure increased compatibility across domains.
  • Using cloud analytics platforms to derive value from IoT / AI data vs physical gateways -pros and cons.
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10:30AM

Ibrahim Gokcen

CTO

Schneider Electric

Associated Talks:

10:30AM - Day 1

View Keynote: Schneider Electric

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Keynote: Schneider Electric

. Ibrahim Gokcen, CTO, Schneider Electric
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11:00AM

Networking Break

11:40AM

Felix Baaken

Head of Omnichannel Analytics

Wirecard

Associated Talks:

12:00PM - Day 1

View Panel: Examining AI uses in Banking & Finance Services

11:40AM - Day 1

View Making the Most of the Power of Payment Data

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Making the Most of the Power of Payment Data

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. Felix Baaken, Head of Omnichannel Analytics, Wirecard
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12:00PM

Joss Langford

Executive Director

Coelition

Associated Talks:

12:00PM - Day 1

View Panel: IoT and AI Data analytics for intelligent decision making

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Lukasz Kuncewicz

Head of Data Science

Enigma Pattern

Associated Talks:

12:00PM - Day 1

View Panel: IoT and AI Data analytics for intelligent decision making

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Faizan Patankar

Venturing and Incubation Lead

Rolls-Royce

Associated Talks:

12:00PM - Day 1

View Panel: IoT and AI Data analytics for intelligent decision making

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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
. Joss Langford, Executive Director, Coelition
. Lukasz Kuncewicz, Head of Data Science , Enigma Pattern
. Faizan Patankar, Venturing and Incubation Lead, Rolls-Royce
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12:40PM

Case Study: Saagie

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01:00PM

Networking Lunch

02:20PM

Simon Fabri

Technical and Product Director

Jaguar Land Rover

Associated Talks:

02:20PM - Day 1

View Solo: Jaguar Land Rover

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Solo: Jaguar Land Rover

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. Simon Fabri, Technical and Product Director, Jaguar Land Rover
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02:50PM

Callum Staff

Lead Data Scientist

Marks and Spencer

Associated Talks:

02:50PM - Day 1

View Panel: Big Data – Creating Intelligent Data Models

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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
. Callum Staff, Lead Data Scientist, Marks and Spencer
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03:30PM

Alejandro Saucedo

Chief Scientist

Institute for Ethical AI & Machine Learning

Associated Talks:

03:30PM - Day 1

View Industry-ready data & machine learning pipelines

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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.

. Alejandro Saucedo, Chief Scientist, Institute for Ethical AI & Machine Learning
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04:00PM

Case study: How to get the most out of Apache Spark

  • Moving from testing and proof-of-concept through to production applications
  • The industries set to be impacted – financial, manufacturing, pharmaceutical
  • Flexibility and adaptability in workloads
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04:20PM

Session Close

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