You’ll wish there were two of you.
This year, Rev 3 will be focusing on building a continuous learning machine into your team (and your life). One where, as data changes, the model improves. Our agenda is split into three tracks: Data Science Management, Model-Driven Businesses and MLOps Tips, Tools and Techniques. Peruse the sessions and times down below…
Building a Well-Oiled Machine: Perfect Your Portfolio Management to Increase Data Science Success
Building a Well-Oiled Machine: Perfect Your Portfolio Management to Increase Data Science Success
Data Science Management
Learn practical strategies and tactics for prioritizing and managing data science projects across the data science lifecycle. Industry leaders will discuss how to develop a pipeline of data science projects, and then prioritize them based upon business value, data readiness, people availability, and more. The best practices revealed in this session will help you manage a portfolio of work across development and production more efficiently, and manage expectations to deliver success.
The Talent Gap and How You Avoid It
The Talent Gap and How You Avoid It
Data Science Management
Avoid the data science talent gap – hear strategies for recruiting, enabling, and retaining the data science talent you need. You'll learn how to create a competency development program to increase the impact of citizen data scientists and drive maximum business value.
Delivering Business Value: Go from Zero to 60...and Stay the Course
Delivering Business Value: Go from Zero to 60...and Stay the Course
Data Science Management
Data science is all about delivering business value. Many organizations focus on time-to-value during model development, but lose sight of (or can't track) value once models are deployed. Join this session to learn how to drive value with integrated workflows that accelerate time to value as well as best practices to ensure that continuous value is being delivered across your deployed models with active monitoring.
To Centralize or Not to Centralize? Choosing the Best Structure for Your Data Science Team's Success
To Centralize or Not to Centralize? Choosing the Best Structure for Your Data Science Team's Success
Data Science Management
Are you trying to determine how to best organize your data science teams to maximize their impact? Many organizations turn to a center of excellence (COE) to provide streamlined support of data science initiatives. Others prefer a more de-centralized approach. Join this panel to hear the pros and cons of how global organizations are structured, as well as the secrets for establishing a successful COE.
The Secrets to Finding Harmony Between Your IT and Data Science Teams
The Secrets to Finding Harmony Between Your IT and Data Science Teams
Data Science Management
Friction – the most common description of the relationship between data science teams and IT. Good enough isn't good enough for model-driven organizations. Success is dependent upon a strong partnership between data science and IT to drive business value without sacrificing risk. Hear from leaders of data science and IT in this fireside chat on how teams can work together to develop a strong partnership for success.
What’s Holding You Back? Unlock the Productivity Secrets to Scaling Data Science
What’s Holding You Back? Unlock the Productivity Secrets to Scaling Data Science
Data Science Management
Many teams struggle to increase project capacity when they don't have the ability to add headcount. In this session we'll explore the hidden barriers to scaling data science, including factors that sap productivity. How do you gain business buy-in to increase capacity? Can reusability and knowledge management help drive capacity? What steps of the data science lifecycle can be automated? Come to this action packed session to hear practical techniques for increasing the capacity of your data science teams.
Maximize Value and Minimize Risk: The Keys to Managing Your Data Science IP
Maximize Value and Minimize Risk: The Keys to Managing Your Data Science IP
Data Science Management
Data science models have become intellectual property that can drive a competitive advantage. Yet, many organizations don't have insight into all of the models within the organization that are driving business processes. To make matters worse, lack of IP management results in duplicate models that distract valuable resources from delivering new incremental value. In this session, you'll hear why a system of record is important and understand how you can leverage your assets to drive additional value without sacrificing risk.
Where Will the Next Modern-Driven Business Revolution Take Place?
Where Will the Next Modern-Driven Business Revolution Take Place?
Model Driven Business
Some industries were born digital and model driven. Others, such as financial services, retail and insurance have rapidly evolved to take advantage of data science. This session will focus on where will model driven businesses will emerge next. Hear from the venture capitalists investing in organizations looking to revolutionize markets to get insights into the organizations that will revolutionize how business is done.
Ethics & ML: Am I About to Blow Up My Company?
Ethics & ML: Am I About to Blow Up My Company?
Model Driven Business
The power of AI and ML can manifest itself for good or evil. As a model driven business you need to navigate the ethical minefield, understanding how your models are being used, how they were built and how they are performing. In this session we will talk about the ethical side of data science, what you need to be watching for and the implications if you don't.
Is Model-Driven Business Defining the Future of Finance?
Is Model-Driven Business Defining the Future of Finance?
Model Driven Business
Financial services have long used data science for lending decisions, risk and customer marketing. In this session we will hear from leaders in the industry on how they are swinging from the rafters to become model driven in areas never previously thought possible.
COVID and the Need for Speed: When Model Velocity Became Imperative
COVID and the Need for Speed: When Model Velocity Became Imperative
Model Driven Business
In spring of 2020 the world changed. All of a sudden every model being used to predict customer behavior, demand, price, and inform supply chains no longer reflected reality. And then reality changed again and again and again. This was true across every industry but none so universally as travel restrictions eliminated or severely restricted demand. That meant that the ability to continuously retrain or replace models, add new data sources and assess model behavior was critical for the survival of the organization. Learn the secrets of success from leaders across multiple sectors of the industry on how they deal with the model velocity imperative in their organization.
Health and Life Sciences: How Models are Transforming an Industry
Health and Life Sciences: How Models are Transforming an Industry
Model Driven Business
The Health and Life Sciences industry is becoming model driven more than ever whether it's in patient care, claims processing, epidemiology or research and development of new treatments. In this session we will hear from leaders in the industry on how they are transforming the way they operate to be more effective, faster, less expensive to make a difference in peoples lives.
Can Your Data Science Keep Up with Rocket Science?
Can Your Data Science Keep Up with Rocket Science?
Model Driven Business
Innovation is the name of the game for the defense industry. That means data science has to keep pace with the demands for new ways to support customer missions. Whether its building an AI- enabled Pilot for DARPA or analyzing manufacturing defects, Lockheed Martin has developed a enterprise capability to rapidly build, deliver and deploy data science at scale that is paying off in terms of bottom line savings and new innovation. In this session you will hear how they made the transformation, where they are going and practical tips for becoming a model driven organization, regardless of your industry.
Big Brother is Coming: How Will You Prepare for Government Regulation & ML?
Big Brother is Coming: How Will You Prepare for Government Regulation & ML?
Model Driven Business
No matter where you do business in the world, its clear that regulation around the use of AI is coming. It may be outright bans on certain techniques such as facial recognition, requirements around regulatory transparency and access or impact assessments for certain types of algorithms. To prepare you need to ensure your data science processes and systems comply with existing and future regulatations, while still retaining the agility needed to adapt to changing business conditions. This panel will shed light on where governments are headed in regulating AI and the steps you should be taking to prepare.
Tuesday @ 08:00 AM
Introduction to Domino for Practitioners (Repeat)
Introduction to Domino for Practitioners (Repeat)
Training
Tuesday, November 9, 8:00 AM – 12:00 PM (240 min.)
Learn about the end-to-end data science lifecycle within Domino. See how to connect to data sources and integrate with Git repositories, work with large data, and develop your model in an interactive environment with easy access to the tools you prefer. Then, deploy the model using a model API, scheduled reports, and an interactive dashboard. Lastly, learn how to work with Domino programmatically.
Tuesday @ 01:00 PM
Managing the Data Science Function in a post-COVID World
Ed Wiley
Data Science Executive, Speaker and Artificial Intelligence & Machine Learning Expert, Ed Wiley Ventures, LLC
Managing the Data Science Function in a post-COVID World
Training
Tuesday, November 9, 1:00 PM – 5:00 PM (240 min.)
The global pandemic triggered our time's largest natural experiment in work culture; organizations across industries were forced to transition in short order to remote approaches to work and management. In data science, leaders were forced to innovate in ways that could not have been imagined only a year before. Now that the world is opening back up, what have data science leaders learned from the last year that can enhance the practice? What aspects of our temporary, remote-first culture should be adopted going forward?
Tuesday @ 01:01 PM
Domino Administration Certification
Domino Administration Certification
Training
Tuesday, November 9, 1:01 PM – 5:01 PM (240 min.)
Learn how to effectively manage both the Kubernetes cluster that runs Domino and the Domino platform itself to enable your team to do their best work. This course covers optimizing Domino to balance cost savings while ensuring optimal performance for your data scientists. You'll also pick up tips on troubleshooting, configuration, and managing resources.
Tuesday @ 01:02 PM
Introduction to Domino for Practitioners
Introduction to Domino for Practitioners
Training
Tuesday, November 9, 1:02 PM – 5:02 PM (240 min.)
Learn about the end-to-end data science lifecycle within Domino. See how to connect to data sources and integrate with Git repositories, work with large data, and develop your model in an interactive environment with easy access to the tools you prefer. Then, deploy the model using a model API, scheduled reports, and an interactive dashboard. Lastly, learn how to work with Domino programmatically.
Tuesday @ 01:03 PM
Advanced Data Science in Domino
Andrea Lowe, PhD
Training and Enablement Engineer, Domino
Advanced Data Science in Domino
Training
Tuesday, November 9, 1:03 PM – 5:03 PM (240 min.)
This course will cover a variety of advanced topics in Domino, including how to add distributed computing into your workflow through on-demand Spark clusters, get the most out of compute environments by learning Docker best practices, enable better collaboration through organizations and advanced Git integration, and establish publishing and deployment pipelines.
Wednesday @ 09:00 AM
Atomic Habits
Atomic Habits
Keynote
Wednesday, November 10, 9:00 AM – 9:35 AM (35 min.)
Atomic Habits is the most comprehensive and practical guide on how to create good habits, break bad ones, and get 1 percent better every day. If you're having trouble changing your habits, the problem isn't you. The problem is your system. Bad habits repeat themselves not because you don't want to change but because you have the wrong system for change. This is one of the core philosophies of Atomic Habits: You do not rise to the level of your goals. You fall to the level of your systems. In this session, you'll get a proven plan that can take you to new heights.
About James Clear
James Clear is a personal development keynote speaker and the author of the #1 New York Times Bestseller Atomic Habits. His entertaining talks teach audiences about small habits, decision-making, and continuous improvement. James doesn't merely report the research of others. He tries out the concepts for himself as he experiments with building better habits as an entrepreneur, writer, and weightlifter. In the end, his talks end up being one-part storytelling, one-part academic research, and one-part personal experiment, forming a colorful blend of inspirational stories, academic science, and hard-earned wisdom. As of 2021, his book Atomic Habits has sold over 4 million copies worldwide, enjoyed over a year on the New York Times bestseller list.
Wednesday @ 11:30 AM
It's the Data, Stupid! How Improving ML Datasets is the Best Way to Improve Model Performance
It's the Data, Stupid! How Improving ML Datasets is the Best Way to Improve Model Performance
MLOPs Tips, Tools and Techniques
Wednesday, November 10, 11:30 AM – 12:15 PM (45 min.)
When working to improve an ML model, many teams will immediately turn to fancy models or hyper parameter tuning to eke out small performance gains. However, the majority of model improvement can come from holding the model code fixed and properly curating the data it's trained on! In this talk, Peter discusses why data curation is a key part of model iteration, some common data and model problems, then discusses how to build workflows + team structures to efficiently identify and fix these problems in order to improve your model performance.
Wednesday @ 12:30 PM
Machine Learning Made Easy With Pycaret
Machine Learning Made Easy With Pycaret
MLOPs Tips, Tools and Techniques
Wednesday, November 10, 12:30 PM – 1:15 PM (45 min.)
PyCaret is an open-source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo on how to use PyCaret in your existing workflows and supercharge your data science team's productivity.
Wednesday @ 02:00 PM
Why Data Scientists Should Build Analytical Apps
Why Data Scientists Should Build Analytical Apps
MLOPs Tips, Tools and Techniques
Wednesday, November 10, 2:00 PM – 2:45 PM (45 min.)
Models, model outputs and APIs aren’t enough. Reports and dashboards aren’t enough. Organizations that are able to extract value from data science and AI initiatives have all realized that specialized analytical apps are almost always needed to connect people to models so as to plug into workflows and change business processes. A stunning 2 out of 5 companies who have invested in AI initiatives report no business impact after three years, according to a report from MIT Sloan and BCG. This is at least partly because of a “last mile failure” where the outputs of AI and data science projects just don’t connect to the rest of the business. Successful data science and AI initiatives in large companies all have something in common: it’s not that they process more data or produce more accurate predictions, it’s that they succeed in changing business processes. Changing processes involves changing people’s behaviour: employees, customers, vendors. They need user-friendly interfaces to the powerful models that drive modern data science. In short, they need analytical apps.
Wednesday @ 02:15 PM
Exposing Machine Learning Models in Real Time at Topdanmark / Current State of Explainable AI on Image Analyses
Exposing Machine Learning Models in Real Time at Topdanmark / Current State of Explainable AI on Image Analyses
MLOPs Tips, Tools and Techniques
Wednesday, November 10, 2:15 PM – 3:00 PM (45 min.)
Wednesday @ 04:30 PM
Every Climb Requires a Unique Approach
Every Climb Requires a Unique Approach
Keynote
Wednesday, November 10, 4:30 PM – 5:05 PM (35 min.)
Every climb requires a unique approach, whether it's up the face of Everest, the corporate ladder or into a position of market leadership. Levine draws upon 18 years of business experience in order to deliver a customized, focused and relevant message to her audiences. Her presentations focus on the parallels between staying alive on the world's most dangerous peaks and succeeding in a fast-paced business world and offer a unique perspective on the topics of leadership, teamwork, innovation and dealing with a changing environment. Her breathtaking visuals will take you on a journey to Mt Everest, the North Pole and up the slopes of mountains from every continent on the planet; where determination is every bit as important as skill when it comes to survival...and willpower is the most essential piece of equipment.
About Alison Levine
Alison Levine is no stranger to risk-taking. She has survived sub-zero temperatures, hurricane-force winds, sudden avalanches, and a career on Wall Street‚ all without the use of supplemental oxygen. Surprisingly, Levine was born with a life-threatening heart condition that precluded most demanding physical activities. As a teenager, her health was so unstable that she was not even allowed to do such basic things as drive a car or walk up stairs. But 13 years after her initial diagnosis she had surgery that changed her life‚ and climbing stairs soon gave way to climbing mountains, a passion that Levine continued to pursue despite her initial health setbacks. In between starting business school and starting a job on Wall Street, Levine‚ accidentally‚ scaled the highest peaks on six continents. Over the next several years as she continued to climb the corporate ladder, she also pushed her limits on the world‚ highest peaks and soon became one of the most experienced female mountaineers in the country.
Thursday @ 09:00 AM
How Experts Make Mistakes and Why Checklists May be the Solution
How Experts Make Mistakes and Why Checklists May be the Solution
Keynote
Thursday, November 11, 9:00 AM – 9:45 AM (45 min.)
The struggle to perform well is universal: each one of us faces fatigue, limited resources, and imperfect abilities in whatever we do. But nowhere is this drive to do better more important than in medicine, where lives are on the line with every decision. In his talk, Atul Gawande explores how doctors strive to close the gap between best intentions and best performance in the face of obstacles that sometimes seem insurmountable.
About Atul Gawande
Dr. Gawande is a practicing general and endocrine surgeon at Brigham and Women's Hospital and professor in both the Department of Health Policy and Management at the Harvard Chan School of Public Health and the Department of Surgery at Harvard Medical School. As a writer, Dr. Gawande is celebrated for his eloquence, depth of thought and research, and broad influence. Dr. Gawande has been staff writer for The New Yorker magazine since 1998, and is the author of four New York Times bestsellers: Complications and Being Mortal: Medicine and What Matters in the End.
Thursday @ 11:15 AM
The Untapped Potential of Vector Embeddings
The Untapped Potential of Vector Embeddings
MLOPs Tips, Tools and Techniques
Thursday, November 11, 11:15 AM – 12:00 PM (45 min.)
Just an array, or the secret ingredient inside the world’s best products? Vector embeddings are used by the most sophisticated ML teams at places like Google, Facebook, Microsoft, and Amazon to make ML products that seem to know you better than your friends do. In this session you will: Discover how those companies use vector embeddings to power semantic search, image/audio search, recommendations, feed ranking, abuse/fraud detection, deduplication, and other applications. Learn where and how you can use vector embeddings in your own applications, from the lab to large-scale production applications.
Thursday @ 12:15 PM
War Stories and Practical Advice for Doing xAI
War Stories and Practical Advice for Doing xAI
MLOPs Tips, Tools and Techniques
Thursday, November 11, 12:15 PM – 1:00 PM (45 min.)
Thursday @ 02:00 PM
Speech to Text in Low Data Regime with Transfer Learning
Speech to Text in Low Data Regime with Transfer Learning
MLOPs Tips, Tools and Techniques
Thursday, November 11, 2:00 PM – 2:45 PM (45 min.)
Thursday @ 04:00 PM
Never Enough: A Navy SEAL Commander on Living a Life of Excellence, Agility and Meaning
Never Enough: A Navy SEAL Commander on Living a Life of Excellence, Agility and Meaning
Keynote
Thursday, November 11, 4:00 PM – 4:30 PM (30 min.)
Leadership is leadership, no matter the setting. And solving difficult problems involves the same kinds of skills and considerations in just about every scenario. Chief among those considerations: an eye on giving back however you can and contributing to the world in your own unique way. We are constantly transforming. We do what we can right now, and then understand that plans will change and industries will evolve. The expectation for the future is that the unexpected will happen. My experience working in the White House for Presidents George W. Bush and Barack Obama taught me this lesson over and over.
About Mike Hayes
Mike Hayes is the Chief Digital Transformation Officer at VMware. He joined VMware in October 2020, and leads the company's worldwide business operations and the acceleration of the company's SaaS transition. Prior to joining VMware, Mike served as Senior Vice President and Head of Strategic Operations for Cognizant Technologies, where he ran a $2B P&L for Cognizant's largest financial services clients. Prior to Bridgewater, he spent 20 years with the U.S. Navy SEALs. Mike Hayes is also the author of "Never Enough," which is about mission, meaning, and impact. All of his profits from Never Enough are being donated to pay off mortgages for Gold Star families, which are families of fallen service members. Copies can be purchased here: https://www.amazon.com/dp/1250753376?tag=macmillan-20