The 2021 Agenda is below – The 2022 Agenda will be here shortly – Meanwhile check out the 2022 Speakers

Predictive Analytics World for Business 2021

May 24-28, 2021 – Livestreamed


 

To view the full 7-track agenda for the six co-located conferences at Machine Learning Week click here or for the individual conference agendas here: PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, PAW Climate or Deep Learning World.

TRACK TOPICS – The two PAW Business tracks cover these topics:
OPERATIONAL
Operationalization, management, best practices
Track 1
TECHNICAL
Machine learning methods & advanced topics
Track 2
TRACK TOPICS – The two PAW Business tracks cover these topics:
OPERATIONAL
Operationalization, management, best practices
Track 1
TECHNICAL
Machine learning methods & advanced topics
Track 2

Session Levels:

Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Orange square sessions are Practitioner Level

All times are Pacific Daylight Time (PDT/UTC-7)

Workshops - Wednesday, May 19th, 2021

7:15 am
Workshop:

Full-day: 7:15am – 2:30pm PDT

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).

The Workshop Description will be available shortly.
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
7:30 am
Workshop:

Full-day: 7:30am – 3:30pm PDT

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.

The Workshop Description will be available shortly.
Instructor
Robert MuenchenUniversity of Tennessee
Manager of Research Computing Support
University of Tennessee

Workshops - Thursday, May 20th, 2021

8:00 am
Workshop:

Full-day: 8:00am – 3:00pm PDT

This workshop dives into the key ensemble approaches, inc   zluding Bagging, Random Forests, and Stochastic Gradient Boosting.

The Workshop Description will be available shortly.
Instructor
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
8:00 am
Workshop:

Full-day: 8:00am – 3:00pm PDT

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general.

The Workshop Description will be available shortly.
Instructor
Clinton BrownleyWhatsApp
Data Scientist
WhatsApp
8:00 am
Workshop:

Full-day: 8:00am – 3:00pm PDT

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.

The Workshop Description will be available shortly.
Instructor
James TaylorDecision Management Solutions
CEO
Decision Management Solutions

Workshops - Friday, May 21st, 2021

7:15 am
Workshop:

Full-day: 7:15am – 2:30pm PDT

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.

The Workshop Description will be available shortly.
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research

Predictive Analytics World for Business - Virtual - Day 1 - Monday, May 24th, 2021

8:45 am
David Stephenson Ph.D.DSI Analytics
Author and Founder
DSI Analytics
9:00 am
MACHINE LEARNING WEEK KEYNOTE
Lessons from: Amazon

Amazon's vision is to be earth's most customer-centric company. This talk explores how the Alexa Hybrid Science team in Pittsburgh, PA applies a customer-centric lens to cutting-edge machine learning research. The team is responsible for developing on-device Alexa automatic speech recognition models to provide a faster, more reliable Alexa experience. Our research includes neural network compression techniques, end-to-end spoken language understanding and optimizing machine learning for edge devices.

Session description
Speaker
Nathan SusanjAmazon.com
Applied Science Manager
Amazon
9:50 am

The quality of any machine learning or deep learning model depends on the values that define the model structure and corresponding hyperparameters. Many practitioners may find themselves investing countless hours manually searching for the right model and related hyperparameter values. Some use highly inefficient grid search methods. Others will use simple random sampling, which actually works fairly well. But alone, this method only offers a globalized search, and other sampling methods may be better suited to the job. 

Why not use machine learning to automate the search for the best model? 

This presentation details an advanced approach that uses both global and local search strategies that can be evaluated in parallel to ensure a quick and efficient exploration of the decision space. In the case of this presentation, a geneticalgorithm (GA) will be examined for the global search because the selection and crossover aspects of a GA distinguish it from a purely random search. A generating set search (GSS) will be used to greedily search the local decision space. 

Session description
Sponsored by
SAS
Speaker
Robert BlanchardSAS
SAS Senior Data Scientist.
SAS
10:10 am
Room change
10:20 am
OPERATIONAL

As companies continue to increase their investment in Machine Learning (ML) and Artificial Intelligence (AI), the focus is increasingly on how to make machine learning pervasive, creating business value by embedding it throughout a company's operations. But what does it take to get out of Proof of Concept purgatory and go from pilots to operational deployments? Do we need more technology, new roles, a different approach or what?

In this session, James will kick off the operational track with a discussion of what it takes to get value at scale from ML. He'll talk about critical issues and how to address them. He'll outline a proven approach that will let non-digital natives and even the most risk-averse companies operationalize and get value from their machine learning investments. Whatever your motivation for using machine learning and no matter how mature (or not) your machine learning team is, you'll learn how to succeed.

Session description
Speaker
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
TECHNICAL
Case Study: National Consumer Panel

Traditionally, successful predictive analytics initiatives have relied on the application of complex algorithms to a lot of numbers and interpreting the output into meaningful language that clients can easily understand. This presentation aims to turn that premise on its head. Starting with a lot of words provided by consumer panelists, NCP analyzed the text provided in their feedback to us and translated those words into numbers. These numbers added up to value for National Consumer Panel. This presentation will share lessons learned from combining text analytics insights with more traditional approaches leveraging quantitative analytics. Attendees can expect to learn how NCP's experience may be extrapolated to provide insights on other business questions.

Session description
Speaker
Thomas SchleicherNational Consumer Panel
Sr. Director, Measurement Science
National Consumer Panel
11:05 am
Break - Visit our Partners' Digital Offers
11:30 am
OPERATIONAL
Case Study: Barron’s Group

Learn how Barron’s Group is leveraging automation and Machine Learning to produce direct-to-reader content and power dynamic editorial tools. This talk will focus on both the technical infrastructure necessary to automate content, and the innumerable product and editorial decisions made throughout the design, development, and deployment phases.

Over the past year, Barron’s Group has used Wordsmith to manage the text generation component of its automated story systems. Auto-generated stories are templated to summarize relevant stock-market events, and may be triggered within our own system to publish under any conditions specified. Because templates can be designed for any relevant use-case, this system is easily extended to different story topics, languages, and business segments. Barron’s currently produces stories on the state of the market at company close, and plans to expand to publish japanese-language translations, automated flash headlines, and morning stock summaries.

Barron’s Group is also in the process of building a Machine Learning-powered system for editorial assists. This tool will rely on anomaly detection performed with Amazon Sagemaker, and will alert editors when noteworthy events occur. In the short-term, this tool is intended as a time-saver for editors who would otherwise have to manually sift through price and volume data for dozens of stocks. In the long term, this kind of technology could be combined with Wordsmith’s templating functionality to produce entire headlines or articles to send off to editors, for review or publication. Here, we will focus primarily on our current process for automated article publication with Wordsmith, along with our work to this point on the Sagemaker editorial assist tool. We will review the results of our work, and discuss future steps that can be taken to expand upon these systems.

Session description
Speaker
Sarah SchmollerDow Jones & Company, Inc.
Software Engineer - ML Automation
Dow Jones & Company, Inc.
TECHNICAL
Case Study: Marriott

With COVID-19 shifting customer behavior the demand for Marriott’s Home and Villas offering has exploded. In this session we will share how deep neural network based Natural Language Generation techniques were used to support Marriott Homes and Villas generating property titles automatically. We will explore different techniques for title generation, provide performance comparisons and lessons learned to activate the model.

Session description
Speakers
Eui-Hong (Sam) Han Ph.D.Marriott International
Vice President, Advanced Data Science
Marriott International
Matt EckertMarriott International
Sr. Data Scientist
Marriott International
12:15 pm

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

12:45 pm
End of Day 1

Predictive Analytics World for Business - Virtual - Day 2 - Tuesday, May 25th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
9:00 am
KEYNOTE

Ethics in AI is no longer optional, and profit and human values are not mutually exclusive. As the scale, scope and speed of AI adoption increase, we are seeing more examples of algorithmic discrimination, automated racism, ageism, deepfakes, large-scale manipulation ("nudging"), and other harms. Much of this is unintentional, but were you to deploy such an app, it could severely damage your organization's reputation and top or bottomline. Because no matter what business you are in, we are all in the business of trust. Join this session to lean how to adopt and scale AI ethically and responsibly.

Session description
Speaker
Natalia ModjeskaInfo-Tech Research Group
Research Director
Omdia (part of Informa Tech)
9:50 am

Predictive analytics reaches out into more and more areas of business, industrial, and research applications. The sheer number of different algorithms and technologies is staggering. In this presentation we give a top-level review of the most popular tree-based algorithms now easily available to anyone through Minitab. From individual trees to powerful modern ensembles, we highlight their strengths, weaknesses, uses, and limitations, especially when compared to the conventional modeling techniques like multiple linear and logistic regression. We illustrate the flexibility of the modern tree-based predictive analytics by building a series of models to predict power generation of a solar energy power plant. In this case, gradient boosting ensemble achieves 20% more accuracy compared to the conventional regression. In addition, the winning model provides great insights into the nature of multivariate dependencies.

Session description
Sponsored by
Minitab
Speaker
Mikhail GolovnyaMinitab
Senior Advisory Data Scientist
Minitab
10:10 am
Room Change
10:20 am
OPERATIONAL
Case Study: Paychex

Due diligence prior to model deployment involves identifying bias risks, features associated with group differences, and potential missing information that could mitigate disparities. Stereotyping, or ascribing common traits to all individuals in a group, is a particular risk for machine learning models, which generate predictions based on feature similarities. Here, I explore stereotyping in regression or classification models. I demonstrate that common fairness metrics are unable to distinguish stereotyping from decisions based on arguably reasonable factors. Therefore, Paychex has developed a “due diligence” script to guide data scientists in assessing disparities. This process uses model outputs and metrics to identify areas of risk, then Shapley explainers with custom references identify features driving differences. Additional reasoning is suggested to investigate the underlying causes of inequalities, consider the effects of missing information that can mitigate risk, and the effects of sensitive features. This structured report format is suitable for stakeholders or oversight committees.

Session description
Speaker
Val CareyPaychex
Data Scientist
Paychex, Inc.
TECHNICAL

For most data science applications, the algorithms do not matter nearly as much collecting the right data and preparing that data properly.  But sometimes, we really need to pull out all the stops, meaning we need to use complex algorithms to solve difficult problems because of the nonlinearity of the data and the existence of unknown but extensive interaction effects between input variables.This talk will describe situations describe what the simpler algorithm do and what they cannot do, and then provide theory and examples for how complex algorithms, like Random Forests, XGBoost and Deep Learning networks, solve those difficult problems. The talk will also include suggestions for how to identify when your problem may benefit from these algorithms, yet these algorithms, in spite of their strengths, don’t necessarily solve the problem.

Session description
Speaker
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
11:05 am
Break - Visit our Partners' Digital Offers
11:30 am
OPERATIONAL

A recurring theme among analytics and data science leaders is the concern of not being able to keep up with all of the rapid change taking place – both individually and as a team. In years past, it was possible to stand up an organization largely made up of analytics generalists who would handle initiatives end to end. In today’s world, it is necessary to utilize a range of specialists focused on either specific methods or specific points in the lifecycle of an analytics and data science initiative. This talk will discuss the trends driving the need to evolve your organization’s talent model, new roles you need to consider implementing, and how they all fit together.

Session description
Speaker
Bill FranksInternational Institute For Analytics
Chief Analytics Officer
International Institute For Analytics
TECHNICAL

Ashish has distilled his experience of building multiple recommendation system into a set of patterns that he's seen emerge again and again. With a knowledge of these patterns, a practitioner can accelerate their time to successful deployment of a sophisticated recommendation system.

Session description
Speaker
Ashish BansalTwitter
Director Recommendations Systems
Twitch
12:15 pm

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

12:45 pm
End of Day 2

Predictive Analytics World for Business - Virtual - Day 3 - Wednesday, May 26th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
9:00 am
Special Plenary

Models generalize best when their complexity matches the problem.  To avoid overfit, practitioners usually trade off accuracy with complexity, measured by the count of parameters.  But this is surprisingly flawed.  For example, a parameter is equivalent to one "degree of freedom" only for regression; it can be > 4 for decision trees, and < 1 for neural networks.   Worse, a major source of complexity -- over-search — remains hidden.  The vast exploration of potential model structures leaves no trace on the final (perhaps simple-looking) model, but has outsized influence over whether it is trustworthy.

I’ll show how Generalized Degrees of Freedom (GDF, by Ye) can be used to measure the full complexity of algorithmic modeling.  This allows one to fairly compare very different models and be more confident about out-of-sample accuracy.  GDF also makes clear how seemingly complex ensemble models avoid overfit, and lastly, reveals a new type of outlier -- cases having high model influence.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
9:50 am

Deep learning models for forecasting and planning have shown significant promise for handling multiple variables, uncovering hidden patterns, and producing accurate forecasts. However, as one might expect, deep learning models are also complex and rife with pitfalls. Since these techniques often seem like a ‘black box,’ managers -- both technical and nontechnical backgrounds -- can find them hard to master.

In this session, Senior Data Scientist, Javed Ahmed will focus on the intuition behind various deep learning approaches, explore how managers can tackle highly complex models by asking the right questions, and evaluating the models with familiar tools.

Attendees at the Metis session will leave with the tools to:

●      Identify types of forecasting applications that can benefit from deep learning 

●      Broadly understand deep learning approaches relevant to forecasting 

●      Understand pitfalls related to deep learning approaches, and why simpler models may work better

●      Evaluate the results of a forecasting program

Session description
Sponsored by
Metis
Speaker
Javed AhmedMetis
Senior Data Scientist
Metis
10:10 am
Room change
10:20 am
OPERATIONAL

Case study of call center analytics work focusing on first call resolution and call reduction. This is a journey through structuring governance around call related projects and value driven approach of prioritizing opportunities as well as solutions. From meta data analysis to NLP models and back once again.

Session description
Speaker
Richard LeeJohn Hancock
Director of Advanced Analytics
John Hancock
TECHNICAL
Lessons from: Microsoft

With the need to cater to a global audience, there is a growing demand for applications to support speech identification/translation/transliteration from one language to another. This talk starts off with a brief introduction to the topic of Machine Translation (MT), the evolution/application of Machine Translation. The focus will be on Neural Machine Translation (NMT). Then it moves on to introduce some of the typical customer cases and finally ends with how to embed such functionality in your application. The talk will end with a Python based solution demo and introduce the audience to some resources and tools that could help them as they continue to explore the domain.

Session description
Speaker
Jaya MathewMicrosoft
Senior Data Scientist
Microsoft
11:05 am

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
OPERATIONAL
Case Study: Career Path

In this talk Dr. Anzelc will provide an overview of her own career path from particle physics PhD to insurance analytics executive to people analytics leader, providing a view of how she approached her job search and developed her career path from individual contributor to team leader. Practical advice on what to do, resources available, and other non-academic career paths of physicists will also be shared

Session description
Speaker
Meghan AnzelcSpencer Stuart
Head of Data & Analytics
Spencer Stuart
TECHNICAL
Lessons from: Google

Companies are frequently faced with large amounts of unstructured text data, like forum comments or product reviews. Important trends can emerge in these datasets, but it can be time-consuming to read through comments, and keyword matching frequently misses critical nuances. We'll discuss how we've approached this problem at Google using Natural Language Processing, with examples of the approach applied to open datasets. We'll explore how this fits into the ML project lifecycle, with examples of common pitfalls. Finally, we'll highlight how to use this technology as part of a "human in the loop" approach to supercharge your existing team members.

Session description
Speaker
Peter GrabowskiGoogle
Software Engineering Manager
Google
12:15 pm
End of Day 3

Predictive Analytics World for Business - Virtual - Day 4 - Thursday, May 27th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
The Session Description will be available shortly.
Session description
Speaker
David Stephenson Ph.D.DSI Analytics
Author and Founder
DSI Analytics
9:00 am
PANEL
Expert Panel

Despite some dystopian predictions, Machine Learning and AI are not about to replace humans completely. But there's concern and confusion about how best to integrate people and machine learning algorithms. Should you have your algorithms recommend actions and allow people to override them? Should you balance human judgment alongside your algorithms, integrating them into a coherent whole? What about capturing the know-how and experience of your people so you can embed it in the algorithm itself? There are many ways to proceed, each with trade-offs around accuracy, organizational adoption, ethics and legality.This panel will discuss the various ways you can combine people and algorithms and maximize the value of both in your operations.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Speakers
Val CareyPaychex
Data Scientist
Paychex, Inc.
Natalia ModjeskaInfo-Tech Research Group
Research Director
Omdia (part of Informa Tech)
Keith McCormick
Data Science Consultant, Trainer, Author, and Speaker
9:50 am

In this presentation, Mark Do Couto, Senior Vice President Data Analytics at Altair, discusses the complexities of Machine Learning and AI. Join this session to hear how Altair is bringing Data Analytics to the masses through a No Coding environment. The future for ML/AI is now and Altair is here to show you how everyone can take advantage of it.

Session description
Sponsored by
Altair
Speaker
Mark Do CoutoAltair
Senior Vice President Data Analytics
Altair

Deep learning models for forecasting and planning have shown significant promise for handling multiple variables, uncovering hidden patterns, and producing accurate forecasts. However, as one might expect, deep learning models are also complex and rife with pitfalls. Since these techniques often seem like a ‘black box,’ managers -- both technical and nontechnical backgrounds -- can find them hard to master.
In this session, Senior Data Scientist, Javed Ahmed will focus on the intuition behind various deep learning approaches, explore how managers can tackle highly complex models by asking the right questions, and evaluating the models with familiar tools.
Attendees at the Metis session will leave with the tools to:
●      Identify types of forecasting applications that can benefit from deep learning 
●      Broadly understand deep learning approaches relevant to forecasting 
●      Understand pitfalls related to deep learning approaches, and why simpler models may work better
●      Evaluate the results of a forecasting program

Session description
Sponsored by
Metis
Speaker
Javed AhmedMetis
Senior Data Scientist
Metis
10:10 am
Room change
10:20 am
OPERATIONAL

About 80% of machine learning projects fail – not because the data scientists failed, but because of the many known and unknown unknowns especially of data, but also of business and users. Consequently, the key to success is to “fail early to succeed sooner” i.e., to identify the critical break points and to focus on the use case with high chances of success. The identification of the right use cases is the purpose of a data strategy and data thinking is the process of thinking through the right solution. This interactive session introduces a method for data thinking, called Data Strategy Design. The free Data Strategy Designkit consists of 15 visual tools to collaboratively and interdisciplinary develop data strategies and data products. It is already used by hundreds of companies worldwide and by participating in this deep dive session you will also learn how to apply the method and tools to your projects. For the joint exercise we will use Miro, an online whiteboard platform, and we will work together on an exemplary machine learning project.

Session description
Speaker
Martin SzugatDatentreiber GmbH
Founder & Managing Director
Datentreiber GmbH
TECHNICAL
Case Study: Booking.com

While hotels' star ratings are widely recognized and used across the globe, there are no similar rating system exists for the large majority of vacation rentals, making it difficult for guests to search and compare options and hard for vacation rental suppliers to market their product effectively.

In this talk, we will present a Quality Rating System for Vacation Rentals based on Machine Learning, focusing on automated explainable vacation rental quality ratings. This system was successfully deployed and validated through Randomized Controlled Trials at Booking.com, impacting millions of accommodations and guests.

Session description
Speaker
Anastasiia KornilovaBooking.com
Machine Learning Scientist
Booking.com
11:05 am

Take a break or join your fellow practitioners in an interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Session description
11:30 am
OPERATIONAL
Case Study: Dow Jones & Company

Modeling the risk of customer churn in large B2B organizations can be tricky. In this session, we will outline our approach to handling that complexity by developing an interpretable model and an interactive front-end tool to highlight risk factors to a team of non-technical stakeholders. We highlight the importance of transparent modeling (as opposed to black-box modeling) in B2B retention, where understanding the risk factors is as important to our customer experience stakeholders as the model itself.

Session description
Speakers
Steve BishopDow Jones & Company, Inc.
Data Scientist
Dow Jones & Company, Inc.
Matt KlubeckDow Jones & Company, Inc.
Data Scientist
Dow Jones & Company, Inc.
TECHNICAL
Case Study: Cape Fear Collective

Systemic racism is a complex and multi-dimensional problem that manifests in several different ways. No single narrative or data point can fully capture the pervasive and damaging nature of this crisis. Through our work at Cape Fear Collective, we attempt to decompose the far-reaching impacts of systemic racism into tangible components. The culmination of this work is a Racial Equity Index, which we believe will aid in the development of strategies and solutions to combat systemic racism and forge a more equitable future.

Session description
Speaker
Nicholas PylypiwCape Fear Collective
Director of Data Science
Cape Fear Collective
12:15 pm
End of Day 4

Predictive Analytics World for Business - Virtual - Day 5 - Friday, May 28th, 2021

8:55 am
9:00 am
KEYNOTE
Humanitarian Applications of ML

A global NGO identifies and rehabilitates tens of thousands of survivors of human trafficking and slavery situations. Different situations necessitate different rehabilitation “journeys.” We analyzed case worker notes from check-ins with survivors to help understand how specific programs, and especially “trauma-informed” programs, might be affecting survivors’ emotional wellbeing and likelihood to finish their journeys. We found generally negative associations between survivor sentiment and interactions with the justice system, and generally positive associations with training programs, especially for rebuilding social support networks and economic empowerment. These findings have implications for journey planning as the NGO tries to scale up its worldwide operations.

Session description
Speaker
Muneeb AlamQuantumBlack, a McKinsey company
Specialist, Data Science
QuantumBlack, a McKinsey company
9:25 am
KEYNOTE
Humanitarian Applications of ML

There are many non-profit organizations trying to help develop and build economic opportunities for families in developing countries around the world by aiding individuals in starting and maintaining small businesses. Vision Fund International is one such organization that uses microfinance loans to jump start business opportunities in these countries. However, these developing countries do not have the credit institutions that countries in the developed world have, which leaves banks restricted on their ability to make loan decisions. Analytical modeling approaches are widely used in major banks in developed countries to efficiently make these decisions. This talk details a project worked on in partnership with Vision Fund International that develops scorecard models for use in loan decisions and the impact it is having on the growth of economies around the world.

Session description
Speaker
Aric LaBarrInstitute for Advanced Analytics at NC State University
Associate Professor of Analytics
Institute for Advanced Analytics at NC State University
9:55 am
The Session Description will be available shortly.
Session description
Sponsored by
Coursera
10:00 am

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Session description
10:20 am
OPERATIONAL
Case Study: Mercer

Most business leaders claim to be "results driven," an orientation reflected in their embrace of a "pay for performance" philosophy. Many performance measures commonly used are “noisy” or contaminated by situational factors far removed from employees’ control. Hence, they fail to capture the actual value that employees deliver, with unintended, negative consequences for employer and employees.

Using case examples, the presenter will show how advanced analytics can address this problem by adjusting performance measures to remove the effects of random or situational factors. By distinguishing employee “value” from “performance.” organizations can deliver more effective incentives at less cost to shareholders.

Session description
Speaker
Haig NalbantianMercer
Senior Partner, Co-leader Mercer Workforce Sciences Institute
Mercer
TECHNICAL
Lessons from: Cisco

Webex Contact Center is a multi-tenanted Cloud Contact Center solution from Cisco, that scales to thousands of concurrent interactions. Improving Agent Performance directly impacts the efficiency and effectiveness of Contact Centers. We are building the next generation AI enabled interaction experiences to create super agents. With the help of AI, agents can now focus on the customer, understand their sentiment and answer questions quickly. AI capabilities that were built include Speech-To-Text transcriptions, sentiment analysis, Question-Answering and Topic Modeling. AI Models by themselves don't become a scalable solution. Serving these models at scale, specifically for a multi-tenanted cloud service, takes additional planning, architectures, capabilities and operations. This session discusses the unique challenges, solutions and takeaways in building and deploying these models and solutions. Key topics addressed are:

- Multi-tenanted Cloud Services: unique considerations
- Model Scaling Challenges for real time
- Architecture options for scaling Models
- Adapting Models to serve at scale
- Tuning Models for Resource & Cost scaling

Session description
Speaker
Kumaran PonnambalamCisco
Director, AI
Cisco Systems, Inc
11:05 am

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
OPERATIONAL

Many analytics teams wait too long to start working on deployment. The most common reason they give is that they want to make sure that the model is “good enough” before they start to plan deployment. The truth is that doing so virtually guarantees project failure, or at a minimum, massive delays. Projects don’t fail because of a failure to find patterns in the data. Projects fail because models that fail to align perfectly with organizational priorities will never be embraced by the end-users of the deployed solution.

Session description
Speaker
Keith McCormick
Data Science Consultant, Trainer, Author, and Speaker
TECHNICAL
Lessons from: Facebook

In this talk we are going to walk through a design of the typical operating recommender system that operates at a large scale. As part of this talk we are going to do a few deep-dives in the various aspects of the recommender systems and will cover various topics that are critical for the recommender system design, but usually do not receive enough attention, such as:

· Desired properties for multi-stage ranking system

· Sparse features for ranking models

· Limitations of the current approaches used in common models and possible ways to address those

Session description
Speaker
Andrey MalevichFacebook
Technical Lead Manager
Facebook
12:15 pm
End of Conference
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