Wisdom 2020 Agenda

Tuesday, February 11 

11:00am – 5:30pm

Registration Open

12:00pm – 4:30pm

Hackathon
Scott Genzer, RapidMiner & Marco Barradas, Master Loyalty Group

Help us give back to our host city of Boston by joining us at the Hackathon! It also happens to the be the home of RapidMiner HQ. Bring your smahts and use your coding and machine learning expertise to dive into what’s happening in the Northeast. Whether we’re figuring out the best location to open a Dunks, calculating the fastest route to Gillette Stadium on game day, or predicting the best places on the Pike for staties to catch speeders, it’ll be a wicked good time. 
To participate, select this additional activity during registration.
No additional fees required to participate.
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RapidMiner Basic Training
Jeffrey Mergler, RapidMiner

In this training, we will discuss the whole RapidMiner platform, and provide hands-on practice developing processes in RapidMiner Studio. These processes will cover key techniques and operators that will be used later in the conference. This is an opportunity to ask questions and make sure you are prepared to get the most out of RapidMiner Wisdom.

To participate, select this additional activity during registration.
No additional fees required to participate.

5:00pm – 7:00pm

Welcome Reception

Wednesday, February 12

7:15am – 5:00pm

Registration Open

7:15am – 8:15am

Breakfast

8:30am – 9:15am

Deepfake is for Losers (and other secret confessions of a data scientist)
Ingo Mierswa, RapidMiner

9:15am – 10:00am

Your Future in Enterprise AI is Bright: You’ve built some models, now what?
Michael Gualtieri, VP, Principal Analyst, Forrester

10:00am – 10:15am

Break

10:15am – 10:50am

AI for Everyone: The RapidMiner vision that puts people at the center of artificial intelligence
Scott Barker & Scott Genzer, RapidMiner

10:55am – 11:25am

Bridging the Gap: Measuring & enhancing integrity with data science
Jeremy Osinski, Senior Manager, Forensic & Integrity Services and Todd Marlin, Principal, Global Forensic & Integrity Services, EY

Using RapidMiner to Support Capital & Maintenance Decision-Making for Linear and Networked Assets
Michael Gloven, Managing Partner, EIS

11:30am – 12:00pm

Finding the Story: How a global creative agency tapped into data science
Brandon Shockley, Director of Research, 160over90

Better Together: RapidMiner and Tableau
Michael Martin, Managing Partner, Information Arts

12:00pm – 1:00pm

Lunch

1:00pm – 1:30pm

Enhancing Quality Control & Transforming Industry 4.0 with AI & IoT
Muddasir Hassan, Data Scientist, Anblicks

Family Feud
With your host Scott Genzer, RapidMiner

1:35pm – 2:05pm

Using the full RapidMiner Platform to Improve Sales and Marketing for the Customer Journey
Elise Watson, Service Lead, Clarkston Consulting

1:35pm – 1:50pm

An Examination of the NFL Quarterback’s Success: Are athletic intangibles a reliable indicator of success?
Heatherly Carlson & Joo Eng Lee-Partridge, Central Connecticut State University

1:50pm – 2:05pm

Data Science for Cybersecurity: Identifying and mitigating threats with RapidMiner
Rodrigo Fuentealba Cartes, Solutions Architect, The Pegasus Group

2:10pm – 2:40pm

Overcoming the Computational Demands of Time Series: Scaling R-based demand forecasting with RapidMiner
Ryan Frederick, Manager of Data Science, Dominos

2:10pm – 2:25pm

Data Mining for the Masses 4th Edition eBook Demo
Matt North, Professor, Utah Valley University

2:25pm – 2:40pm

Teaching Rapidly: Using RapidMiner in education
Tamilla Triantoro, Professor, Quinnipiac University

2:40pm – 2:55pm

Break

2:55pm – 3:25pm

Call Volume Forecast using RapidMiner
Michael Stansky, Consultant, Data Analytics, FirstEnergy

2:55pm – 3:10pm

Clustering as the Part of the Data Science Methodology
Lionel Der Krikorian, LDK 360

3:10pm – 3:25pm

Post “One-Size-Fits-All” Healthcare: Welcome to the new era of personalized medicine
Sven Van Poucke, MD, PhD, ZOL Genk

3:30pm – 4:00pm

We Need Smarter Bots: The rapid evolution of the automation ecosystem
Mahesh Vinayagam, Founder & CEO, qBotica

3:30pm – 3:50pm

A New Python Operator Framework
Martin Schmitz, Head of Data Science Services, RapidMiner

3:50pm – 4:10pm

New Tools from RapidMiner Labs
Gisa Meier, Software Engineer, RapidMiner

4:05pm – 4:35pm

FutureBright Analytics: How an international education company uses analytics to power growth and student success at scale
Brian Meagher, Vice President, Analytics, Shorelight Education

4:10pm – 4:30pm

The Web App Builder Extension: A new way of deploying machine learning projects using web apps
Christian König, Data Science Coach, Old World Computing

4:35pm – 4:45pm

Break

4:45pm – 5:30pm

Panel: Getting data science projects over the finish line – What does it take?
Brian Tvenstrup, Lindon Ventures
Cody Lougee, Vantage West
Martin Schmitz, RapidMiner
Paul Simpson, Elliott Davis
Moderated by: Michael Martin, Information Arts

Session Details

Keynotes

 

Deepfake is for Losers (and other secret confessions of a data scientist)
Ingo Mierswa, RapidMiner

Kaggle is useless. Most models don’t provide business impact. Data scientists are wasting time. And deepfake is for losers. Dr. Ingo Mierswa has worked for 20 years on hundreds of data science projects, and in that time, he’s seen it all. In this presentation, he’ll discuss some of the mistakes we make as data scientists – often inadvertently, but sometimes even though we should know better. What’s worse, the complexity of the data science field lets us hide the consequences of these mistakes from others. So how do we break free? Dr. Mierswa will present a manifesto for data science, a set of basic principles designed to guide our work and make sure that our models have the desired impact.

Your Future in Enterprise AI is Bright: You’ve built some models, now what?
Michael Gualtieri, Forrester

AI is real and ready. Data science teams are the stars because they use ML to craft valuable use cases to augment intelligence, automate decisions, hyper-personalize customer experiences, streamline operational processes, and much more. However, for most enterprise technology leaders, AI technologies and use cases are still far too mysterious. Enterprise leaders and data scientists must forge a coherent, pragmatic AI implementation strategy without missteps to scale use cases throughout the enterprise.

In this keynote, Forrester Research Vice President & Principal Analyst Mike Gualtieri will demystify enterprise AI, identify use cases most likely to succeed, and, most importantly, provide research-based advice to enterprise leaders and data scientists that are charged with moving AI forward in their organization.

AI for Everyone: The RapidMiner vision that puts people at the center of artificial intelligence
Scott Barker & Scott Genzer, RapidMiner

The secret to successful implementations of AI in the modern enterprise isn’t learning a specific coding language, using a fancy new algorithm or following a formulaic process that worked well for someone else. It’s people. The most ironic truth in buzzy world of machine learning and artificial intelligence is that humans are the key to success. This doesn’t mean you need to find and hire a unicorn data scientist who has a PhD in statistics and computer science, Steve Jobs-like presentation skills and an unprecedented grasp on business strategy. Rather, it’s all about re-thinking the way diverse teams of people create, communicate and operate together from engineers, data analysts, and data scientists to executives and key stakeholdersIf data science projects happen in a vacuum they will be destined to fail. RapidMiner’s visual workflow design has helped drive collaboration across modern enterprise data science initiatives, enabling a true multi-disciplinary approach. We’re excited to show you our latest advances that will make it even easier for teams to work together towards the same end goal of driving change and shaping the future of their business with AI/ML 

This session will: 

  • Unveil the new RapidMiner mission and product strategy that’s designed to help put people at the center of your machine learning journey 
  • Show how our roadmap is built around leveraging different team members in order to make your projects successful 
  • Illustrate and demo some of the newest features – including an early glimpse at RapidMiner 9.6 

Panel: Getting data science projects over the finish line – What does it take?
Brian Tvenstrup, Lindon Ventures
Cody Lougee, Vantage West
Martin Schmitz, RapidMiner
Paul Simpson, Elliott Davis
Moderated by: Michael Martin, Information Arts

Data science projects often have participants with a range of skills and backgrounds. This means a given team will have a variety of different ideas and expectations about what to do and how to work together. This panel will bring together these different perspectives—including domain experts, data scientists, business users, and managers—to discuss their differing perspectives and how they can best to work collaboratively to ensure that data science projects have the desired impact and avoid contributing to the Model Impact Epidemic.

Business Track

Hear from organizations using RapidMiner to drive revenue, reduce costs, and avoid risks.

Bridging the Gap: measuring & enhancing integrity with data science
Jeremy Osinski and Todd Marlin, EY

How can you quantify someone’s integrity? How can businesses bridge the gap between their employees’ and stakeholders’ intentions, actions and data? During this session we’ll explore how to use data science to make an organization’s legal and regulatory compliance and risk management processes more effective and efficient. By integrating multiple, disparate data sources to developing digestible front-end visualizations and case management tools combined with machine learning, we are utilizing data science to drive improved organizational culture and a better-functioning business environment.

Finding the Story: How a global creative agency tapped into data science
Brandon Shockley, 160over90

This session Brandon will describe the agency’s data mining journey, from early prototypes to actionable consumer insights. Attendees will learn creative ways to apply machine learning to market research for customer segmentation, messaging, and brand health tracking.

Enhancing Quality Control & Transforming Industry 4.0 with AI & IoT
Muddasir Hassan, Anblicks

The automobile industry is a highly regulated industry that is slowly adopting Industry 4.0 to transform their operations. Our customer is a global automobile manufacturer who wants to succeed in the ultra-competitive engine manufacturing industry by delivering high-quality engines in tight timeframes. Manual quality inspection methods are very difficult and time-consuming leading to more challenges in process optimization and scaling.  Join us as we discuss how we solved our customer’s challenge by implementing artificial intelligence (AI) and IoT solutions to the manufacturing process, using RapidMiner data science platform to speed up the fault detection process and predict crucial defects faster and more accurately. The AI solution analyzes the IoT data from 110+ IoT devices including engine temperature, pressure, air, cooling sensors and others that are used in the manufacturing process. 

Using the Full RapidMiner Platform to Improve Sales and Marketing for the Customer Journey
Elise Watson, Clarkston Consulting

Data has become an essential asset for companies who want to better understand the customer journey. It gives insight into who your customers are, how they engage with your sales and marketing tactics, and the impact of each interaction, to help businesses know the best way to spend to increase sales. At a pharmaceutical company, we are using the RapidMiner suite to prepare, model and display data in several use cases that explain and enhance the health care professional’s journey, including target identification for a variety of initiatives and providing personalized marketing strategies.

Overcoming the Computational Demands of Time Series: Scaling R-based demand forecasting with RapidMiner
Ryan Frederick, Dominos

Forecasting demand across the supply chain is crucial for an organization that prides itself on reliable service and speedy product delivery. See how the data science team at Domino’s tackled the challenge and worked through a complex time series forecasting exercise – from prototype to delivery – and uncovered an innovative way to scale R-based time series models to drive reduced errors and faster runtime.

Call Volume Forecast Using RapidMiner
Michael Stansky, FirstEnergy

In this session, Michael will demonstrate how to use RapidMiner to forecast customer contact center call volume using historical all volume and considering call volume drivers. 

We Need Smarter Bots: The rapid evolution of the automation ecosystem
Mahesh Vinayagam, qBotica

The automation ecosystem is rapidly evolving and bots need to become smarter.  This new ecosystem is revolutionizing every industry through its ubiquitous ability to work with any software system, infrastructure and any industry domain. Recently, there is much discussion around whether RPA is a temporary solution with a failing Return of Investment and the adoption is more of a hype than actually empowering Digital Transformation. This session will discuss the merits of utilizing the Automation Ecosystem to re-imagine the Future of Efficiency in enterprises without undertaking multi-year Digital Transformation projects. We will also discuss what leads RPA initiatives to fail on Return on Investment promises and how to avoid this. Finally, discussions circling the evolution of RPA into Intelligent Automation using technologies such as Machine Learning, Artificial Intelligence and Computer Vision for a Cognitive Future.

FutureBright Analytics: How an international education company uses analytics to power growth and student success at scale
Brian Meagher, Shorelight Education

Shorelight brings together universities and international students with the goal to help educate the world.  In just five short years, Shorelight has grown to enroll more than 10,000 students from 120 countries, all while maintaining a first year completion rate of 90%.  In this session, we’ll unpack how Shorelight uses data science to help fuel growth and student success, particularly how to convert the right students to the right universities.

User Track Sessions

The user track was designed by the users for the users. It will consist of 30 minute sessions and lightning demos. 

Using RapidMiner to Support Capital & Maintenance Decision-Making for Linear and Networked Assets
Michael Gloven, EIS

This session will outline how to use RapidMiner to support investment & maintenance decision-making for linear and networked assets such as pipelines, roads, electric transmission lines, water distribution systems, etc. Join as Michael demonstrates how machine learning can predict undesirable events and monetized risk for linear and networked assets. These results may then be used to support specific risk mitigation strategies and budget plans. The objective is to put in place a more strategic data-driven approach to resource decision-making, which should improve the risk profile and profitability of the asset owner. Key to the presentation will be demonstrating important considerations for the application of machine learning to these types of assets.

Better Together: RapidMiner and Tableau
Michael Martin, Information Arts

This session will be a demonstration of the RapidMiner | Tableau Integration component developed by Bhupendra Patil of RapidMiner for classification and association mining.

Family Feud
Team RapidMiner vs. Team Unicorns with your host Scott Genzer, RapidMiner

Do you know the output ports of the Handle Exceptions operator? The hyperparameter options of Neural Net? We’re putting four of RapidMiner’s top engineering & customer success team members head-to-head against our top RapidMiner Unicorns for fame and fortune in this fun takeoff of the TV game show “Family Feud”. Come play along and cheer on “Team RapidMiner” and “Team Unicorns”.

A new Python operator framework
Martin Schmitz, RapidMiner

Python is the dominating programming language for Data Science. RapidMiner’s Python Scripting extension already provided a way how to integrate Python into RapidMiner. With the new Python Operating Framework (PoF) there is a new option how to work with Python within RapidMiner. PoF allows anybody to write new operators – in Python! No need for Java anymore. You can just package your favorite Python scripts into an operator and share it with anybody.

The Web App Builder Extension: A new way of deploying machine learning projects using web apps
Christian König, Old World Computing

In the real world many projects in the domain of machine learning face problems with the deployment of the solution. In many cases there’s a too limited understanding about machine learning to specify the target solution at all. Hence a data scientist needs to approach that in an agile way, which requires the ability to swiftly create end user interfaces to showcase results and make them “feelable”. After showcasing, the results need to be reusable for real deployment in order to not waste money, effort, and time. Christian will demonstrate a new extension that adds these abilities to the RapidMiner platform in a flexible and seamless way. RapidMiner processes are used to build the app and specify the data logic behind it.

User Track Lightning Demos

These lightning demos are 10 minute presentations where users show us something cool that they’ve done with RapidMiner. 

An Examination of the NFL Quarterback’s Success: Are athletic intangibles a reliable indicator of success?
Heatherly Carlson & Joo Eng Lee-Partridge, Central Connecticut State University

For many years, sports analytics have demonstrated a robust relationship between NFL draft measurements and NFL success. In particular, NFL drafts have been harnessing the power of sports analytics to predict the future value of the quarterback. However, most of these pre-draft metrics deal with the physical prowess or prior physical achievements of the quarterback. While these measurements may provide some estimate of the quarterback’s predicted value in the NFL, we are proposing looking at other intangible variables to provide an alternate indicator of future value. Some potential intangible variables that could predict to QB success include character risk, injury resilience, psychological variables, environmental variables, adversity, cognitive ability, motivation and leadership experience. Heatherly & Joo Eng will explore the relationship between the player intangibles and quarterback outcome measures such as number of playoff game appearances, number of years as starting QB and whether they have ever taken their team to a playoff game. They will use both supervised and unsupervised machine learning to provide insights in QB success and QB intangible variables and provide recommendations for future QB drafts.

Data Science for Cybersecurity: Identifying and mitigating threats with RapidMiner
Rodrigo Fuentealba Cartes, The Pegasus Group

Data Science meets Cybersecurity to protect your Web application from bots: in this small demonstration, Rodrigo will explain a proof of concept architecture he uses to score HTTP requests, detect attackers and block them using RapidMiner Real Time Scoring, making use of open source tools such as rsyslog, a small agent written in Python and iptables.

Data Mining for the Masses 4th Edition eBook Demo
Matt North, Utah Valley University

Since publication of its first edition back in 2012, Data Mining for the Masses has become a staple gateway text for learning data science and analytics using RapidMiner. This presentation reviews the latest enhancements to the book, now published in its fourth edition as an interactive, smart textbook on the MyEducator platform.

Teaching Rapidly: Using RapidMiner in education
Tamilla Triantoro, Quinnipiac University

This semester Tamilla had to develop a new Data Mining/Machine Learning course for senior undergraduate students. Selecting an appropriate platform was quite a journey! She wanted to try something new and not code-based, so she developed a checklist that included teaching needs, GUI requirements, software reputation, availability of educational licenses, ease of use and educational resources. After some search and comparison, Tamilla landed on RapidMiner. In this session, she will present how she adopted RapidMiner for teaching machine learning concepts, including topics and techniques she covered in class, student feedback, and the support she received from RapidMiner unicorns. 

Clustering as the Part of the Data Science Methodology
Lionel Der Krikorian, LDK 360

There will be two parts to this presentation and Lionel will demonstrate both of these techniques using the Titanic dataset. 

Using Clustering for Preprocessing: Clustering can be an efficient approach to dimensionality reduction, in particular as a preprocessing step before a supervised learning algorithm.

Using clustering for semi-supervised learning: Another use case for clustering is in semi-supervised learning, when we have plenty of unlabeled instances and very few labeled instances. 

Post “One-Size-Fits-All” Healthcare: Welcome to the new era of personalized medicine
Sven Van Poucke, MD, PhD, ZOL Genk

Sven will discuss the current position of RapidMiner as a tool for personalized medicine and what is needed to promote the understanding and adoption of RapidMiner in the new era of personalized medicine, genomics, etc.