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Revolutionizing Offline Retail Pricing & Promotions with ML — Daniel Guhl
Mar
15
10:30 AM10:30

Revolutionizing Offline Retail Pricing & Promotions with ML — Daniel Guhl

Everybody uses price promotions in retail. However, individual pricing is seldom used, particularly in offline retail. Marketing literature has been advocating the use of individual price discrimination for decades. Furthermore, product recommendations, ever-present in e-commerce, are also not often found in offline retail. We show the machine learning driven system behind a new promotion channel that enables retailers and manufacturers alike to target individual customers in offline retail. Lessons learned, technologies used, and machine learning approaches driving our system will be shown.

Daniel Guhl has a background in economics & marketing, and got interested in data modeling during his Ph.D.. Currently, he is working as a data scientist at a Berlin based Start-up and is pursuing a postdoc at Humboldt University. He enjoys learning everyday and focuses on solving real world problems.

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Jacek Dabrowski has a university background in mathematics, computer science and psychology. He is also a startup veteran with experience in financial technology and online advertising. His current focus is on building distributed real-time systems, big data pipelines and machine learning engines. He is also passionate about deep learning applications.

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How to predict the future of shopping — Ulrich Kerzel
Mar
15
10:00 AM10:00

How to predict the future of shopping — Ulrich Kerzel

Shopping, or as the people on the other side of the counter call it, retail has become the number one breeding ground for predictive applications in the enterprise. What started as simple recommendation engines has evolved into a complex and powerful ecosystem of predictive applications that affect core processes such as pricing, replenishment and staff planning. In this talk, Ulrich Kerzel will share impact and experiences from building and operating predictive applications for large retailers, and explain why the future of retail is as much a science as an art.

Dr. Ulrich Kerzel is a Senior data scientists at Blue Yonder and renowned scientist with research experience at the University of Cambridge and CERN. Ulrich Kerzel earned his PhD under Professor Dr Feindt at the US Fermi National Laboratory and at that time made a considerable contribution to core technology of NeuroBayes. After his PhD, he went to the University of Cambridge, were he was a Senior Research Fellow at Magdelene College. His research work focused on complex statistical analyses to understand the origin of matter and antimatter using data from the LHCb experiment at the Large Hadron Collider at CERN, the world’s biggest research institute for particle physics. He continued this work as a Research Fellow at CERN before he came to Blue Yonder as a senior data scientist.

Twitter: @Ukerzel - Linkedin

 

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The emergent opportunity of Big Data for Social Good — Nuria Oliver
Mar
15
9:10 AM09:10

The emergent opportunity of Big Data for Social Good — Nuria Oliver

Nuria Oliver is a computer scientist and Scientific Director at Telefónica. She holds a Ph.D. from the Media Lab at MIT. She is one of the most cited female computer scientist in Spain, with her research having been cited by more than 8900 publications. She is well known for her work in computational models of human behavior, human computer-interaction, intelligent user interfaces, mobile computing and big data for social good.

Twitter: @nuriaoliver - LinkedIn

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Predictive Analytics Use Cases in the Manufacturing Industry — David Arnu
Mar
14
10:45 AM10:45

Predictive Analytics Use Cases in the Manufacturing Industry — David Arnu

Predictive analytics has become essential for companies in all sectors, including the industrial sector, to leverage the potential of their data and to stay competitive. A frequent use case is predictive maintenance, i.e. predicting and preventing machine failures. This avoids unplanned production stops and maintenance, lowers costs, and improves planning and reliability. Another important use case is quality assurance. Production issues can be detected very early on, i.e. to optimize product design, or production processes can be optimized. Risk management is important for industry companies to identify potential risks that might impact future productions. Future developments can be predicted accurately and costs can be stabilized. Especially for industry firms, productions losses, i.e. caused by interruptions in the supply chain, lead to great issues and costs, because compliance can’t be fulfilled or production isn’t at full capacity.

The “Funded R&D” team of RapidMiner mainly works on research projects and innovative predictive analytics use cases, either funded by the European Union (e.g. within the “Seventh Framework Programme” and “Horizon 2020”) or the German government. Also the US government recognized the importance of predictive analytics and supports research projects that deal with predictive analytics in the industry. The advantage of research projects is that business and research come together and find solutions for relevant industry challenges using innovative technology and algorithms that are applied on real world scenarios at our industrial partners.

RapidMiner started as a research project at Technical University of Dortmund in 2001 and evolved to a widely used open-source code-free predictive analytics platform. Due to our academic roots, RapidMiner still keeps a strong interest in research and performs numerous joint R&D projects with universities. To gain experience in current topics and solve real issues of industry partners, we founded the “Funded R&D” team, which focusses on research projects with innovative predictive analytics use cases.

Within the ProMondi project we created a solution for predicting assembly plans and costs for new product designs using Data and Text Mining and thereby support product designers and assembly planners, enabling better and faster decision making in product design. This solution was successfully applied to product designs and assembly plans of truck engines, washing machines, and dish washers, making the partner companies more agile and reducing their costs. In the FEE project we work together with partners from academia, the chemical industry and industrial automation to early detect critical situations and to support plant operators in their decision making. We’ll create a solution integrated into existing software that helps plant operators and plant managers to detect and react proactively in critical situations. Therefore we use Big Data technology to analyze time series data from sensor readings and unstructured data from log entries (created automatically as well as manually). The change from reactive to proactive handling of critical situations saves costs that were caused by production losses.

With this presentation we share our experience and best practices with predictive analytics use cases in the manufacturing industry. Which use cases occur frequently? How can typical use cases in the industry best be solved? What role plays predictive analytics in those solutions and how can the solutions be integrated in your daily business?

David Arnu studied at University of Dortmund and holds a Master of Science degree in Computer Science. There he worked as a research assistant at the chair of Artificial Intelligence, later he joined the R&D team of RapidMiner as Software Engineer and Project Engineer. Now, he is a Data Scientist at RapidMiner. He is working on research projects focussing on Predictive Analytics and Big Data.

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Real-world applications of AI — Daniel Hulme
Mar
14
10:15 AM10:15

Real-world applications of AI — Daniel Hulme

This talk will offer answers to the following questions: What is data-driven decision making? What is AI? What is Business Intelligence? Why are these concepts important? What are the biggest challenges and opportunities?

Daniel is the CEO of Satalia that provides AI inspired solutions to solve industries hardest problems. He’s the co-founder of the ASI that transitions scientists into data scientists. Daniel has a MSci and EngD in AI from UCL, and is Director of UCL’s Business Analytics MSc; applying AI to solve business/social problems. Daniel has many Advisory and Executive positions, holds an international Kauffman Global Entrepreneur Scholarship and actively promotes innovation across the globe.

Twitter: @TheSolveEngine - Linkedin

 

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Building a production-ready predictive app for customer service — Alex Ingerman
Mar
14
9:45 AM09:45

Building a production-ready predictive app for customer service — Alex Ingerman

Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how a smart application for predictive customer service can be built in the AWS cloud. We will walk through the process of labeling data, setting up a real-time data ingestion pipeline and using machine learning to make real-time predictions for messages arriving via social media channels. You will be able to later replicate everything shown on your own, using the provided sample code and training dataset.

Alex Ingerman leads the product management team for Amazon Machine Learning. He joined Amazon in 2012, after working on products including web-scale search, content recommendation systems, immersive data exploration environments, and enterprise email and content servers. Alex holds a Bachelor of Science degree in Computer Science, and a Master of Science degree in Medical Engineering.

Twitter: @alex_ingerman - Linkedin

 

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A business-level introduction to AI — Louis Dorard
Mar
14
9:10 AM09:10

A business-level introduction to AI — Louis Dorard

Artificial Intelligence and Machine Learning are becoming increasingly accessible. Starting from example use cases, I’ll aim at demystifying how they work and how they improve businesses in 3 areas: increasing the number of customers, serving them better, and serving them more efficiently. I’ll show how machines can use data to automatically learn business rules and make predictions, that can then be used to make better decisions. I’ll introduce the main concepts of ML, its possibilities, its limitations, and I’ll give tips on framing the right problems for your company to tackle.

Louis Dorard is the author of Bootstrapping Machine Learning, a co-founder of PAPIs­, and an independent consultant. His goal is to help people use new machine learning technologies to make their apps and businesses smarter. He does this by writing, speaking and teaching.

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