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Agenda
Predictive Analytics World for Industry 4.0 2023
June 18-22, 2023 l Red Rock Casino Resort & Spa, Las Vegas
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.
Session Levels:
Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Workshops - Sunday, June 18th, 2023
Full-day: 8:30am – 4:30pm PDT
This one day workshop reviews major big data success stories that have transformed businesses and created new markets.
Full-day: 8:30am – 4:30pm PDT
Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.
Workshops - Monday, June 19th, 2023
Full-day: 8:30am – 4:30pm PDT
This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting.
Full-day: 8:30am – 4:30pm PDT
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).
Full-day: 8:30am – 4:30pm PDT
Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.
Full-day: 8:30am – 4:30pm PDT
This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning.
Predictive Analytics World for Industry 4.0 - Las Vegas - Day 1 - Tuesday, June 20th, 2023
Machine Learning Week Founder Eric Siegel will welcome you to the event and kick things off.
In this informal but informative presentation, Dr. James McCaffrey – who leads Microsoft Research’s CEO-mandated initiative to transfer deep learning intelligence into all products, services, and supporting systems across the enterprise – will describe six of the latest trends in machine learning and deep neural systems. Today's hottest trends include the deployment of generative AI systems like GPT-x/ChatGPT, Codex, Copilot, and DALL-E – yet they also include innovative deployments of more classical ML methods. The emphasis will be on information that is practical and can drive revenue, not theoretical ideas filled with math equations and Greek letters. Particular emphasis will be given to explaining the difference between systems that require massive resources or expensive licensing, and systems that can be successfully implemented by organizations with a limited budget.
Google continues to take a bold and responsible approach to developing and deploying AI through the company’s infrastructure, tools, products, and services. Google brought further AI breakthroughs into the real world through Google Cloud’s launch of the next wave of generative AI across core areas of their business, and new partnerships and programs grounded in Google’s commitment to an open AI ecosystem. At the same time, AI, as a still-emerging technology, poses complexities and risks; and the development and use of AI must address these risks in a structured, transparent, and accountable way. A robust governance structure – and rigorous testing and ethics reviews — is necessary to put responsible AI principles into practice. And with AI regulation coming soon, Jen will share learnings, challenges, and practical tips on how Google is maturing its responsible AI practices, processes, and tools in advance of greater regulatory, global standards, and consumer expectations.
Understanding how to evaluate the performance of a model is fundamental to the work of Decision Science professionals. Often, metrics used to evaluate model performance are deployed based on theory and that might not, necessarily, holistically explain the models applied benefit, or lack thereof, to the business. This session will evaluate the trade off between model performance metrics and making business impact. What is good enough?
IoT sensors are commonly used to monitor the environment inside shipping containers. Such monitoring provides valuable insights on the condition of cargo in real time. In many situations, however, guidance on preventive action (packaging, desiccant quantity etc.) is desired in the planning phase, i.e., before the first shipment has departed. In such situations, we can use the itinerary and weather data to model the climate inside the container. One key challenge in such modeling arises from unknowns. For example, an exposed container's climate is different from one stacked below others. Mapping weather + itinerary data to container climate is a representation learning problem, where the representation signifies the container's journey. Statistical characterization of container representations can inform the shipment planning process.
During the identification of a problem and root cause analysis, traditional approaches include employing statistical design of experiments (DOE). In this presentation, the limitations of DOE will be compared to the advantages of utilizing "Big Data" and machine learning to find solutions. A case study from a manufacturing process using synthetic data highlights the ability to find "hidden variables" that lead to better solutions. By examining data from a whole process rather a narrow band available through DOE, the investigation is more effective with better results. The benefits of machine learning and model tournaments are highlighted.
As Industrial organizations adventure in their advance analytics and AI journey to gain more insights from their data, organizations need to evolve and mature in the way they can interact and leverage data. These organizations need to deploy individuals who can provide the vision for this journey and lead their organizations how navigate it.
This is where the Analytic Translator role is essential to an organization's success. They are the visionary and guide to create a path from being overwhelmed by ineffective data and metrics within undefined business processes to effectively "organizing information" to inform key business decisions. This talk will discuss the role of the Analytic Translator to enable the advance analytics and AI journey, what skills are needed, and how they can lead their organizations effectively to secure the desired competitive advantage.
At Northwestern we have developed a system that is built to consume and capitalize on IoT infrastructure by ingesting device data and employing modern machine learning approaches to infer the status of various components of the IoT system. It is built on several open source components with state-of-the-art artificial intelligence. We will discuss its distinguishing features of being Kubernetes native and by employing our work that enables features to be specified through a flexible logic which is propagated throughout the architectural components. We will discuss select use cases implemented in the platform and the underlying benefits. The audience will learn how to build or use a streaming solution based on Kubernetes and open source components.
Industry 4.0 is here. AI, big data, the cloud, robotics, and smart devices are the enablers for building next-gen businesses. We need to focus on building skills to use these technologies that can dramatically multiply any business by strategically using data’s latent, transformative potential.
Join our speaker Asha Saxena CEO at Women Leaders in Data and AI [WLDA.TECH], Adjunct Professor at Columbia University and Author of The AI Factor, discuss the best practices, frameworks and how-to build tangible skills as a leader in today's business world.
Predictive Analytics World for Industry 4.0 - Las Vegas - Day 2 - Wednesday, June 21st, 2023
Within the Human Resources industry there are still many challenges ahead, for example, keyword-based searches or manual resume screening could contribute to omitting valuable opportunities or lengthy hiring procedures. For this reason, we would like to present a novel Recommendation System in the field of HR, which includes a series of Natural Language Processing techniques and Deep Learning models, that allow us to achieve a fully automated process that will propose semantically related and explainable suggestions to job seekers and companies in real-time. The system architecture combines several NLP techniques, such as named entity recognition, text classification, and entity relationships based on HR data, to extract and process more than 50 different characteristics of job postings and resumes, such as occupations, skills, education, etc., with various granularity levels and multiple languages.
We illustrate here a Living Digital Twin of a fleet of Electric Vehicles that gives actionable predictions of battery degradation over time. Since each vehicle takes a different route and has different charging and discharging cycles over its lifetime, the battery degradation for each vehicle will be different. We use a scalable predictive modeling framework deployed across a distributed computing architecture in the cloud to make individualized predictions for each EV battery in the fleet to reflect the degraded performance accurately. The neural network battery model is calibrated using the parameters that had the most significant impact on the output voltage through the Unscented Kalman Filter (UKF) method, which is a Bayesian technique for parameter estimation of non-linear system behavior. We simulated in-production real-world operations by having the vehicles “drive” the routes using synthetic datasets and showed how the calibrated model provide more accurate estimates of battery degradation. Using a Living Digital Twin to calculate the remaining range and battery State of Health addresses problems of range anxiety in the EV automotive industry and can drive the value of EVs in the market.
ML’s great strength is that example cases are all you need to create a predictive model. The predictions work as long as the underlying process is not tampered with. But clients usually seek more: they yearn to understand the "data-generating machinery” in order to improve the outcome that the model predicts. Yet, this is dangerous without additional external information, including the direction of influence between variables. This talk illustrates how to achieve “peak interpretability” by using influence diagrams to model causal relationships, avoid mistaking correlation for causation, and quantify how outcomes will change when we manipulate key values.
The largest issue ML teams face is due to AI models that silently fail. Silent failure occurs when model performances gradually degrade over time without showing any apparent signs of failure. These signs are therefore difficult to catch in time, usually leading to sudden or abrupt drops in performance after the gradual decline. This leads to a heavy impact on not just ML or business teams but also on the customer who faces the repercussions of incorrect predictions.
Pioneering tech giants have been managing silent model failures with AI Observability, and the positive results continue to encourage the AI industry to adopt observability practices. Observable AI enables a continuous collection of data from multiple touchpoints to deliver insights for improved model performance in production. It can be broken down into three high-level components: Monitoring, Explainability, and Accountability.
In this session, you will learn how to:- automate model monitoring
- dive deeper with root cause analysis to explain model decisions
- proactively troubleshoot your models to build reliable and compliant solutions that are resistant to silent model failures
Supply chains have been very lean for decades, and the challenges in the last few years have led more teams to focus on resiliency. With the quantity of data available across manufacturing, shipping, stocking, and inventory, teams can turn to analytics to improve the resiliency of supply chains. This talk will highlight how analytics has helped improve supplier reliability, decrease sourcing costs, and quantify the true risk of supply chain disruption.
Ask our “Rockstars” anything about predictive analytics! Curious about machine learning tips, tricks, best practices and more? This is your opportunity to hear advice directly from the experts. The hardest part of machine learning can be getting models deployed into production. Our panel has done it, and is willing to spill the tea on how to make it happen. You’ll want to stick around for this ultimate session of the conference.
Workshops - Thursday, June 22nd, 2023
Full-day: 8:30am – 4:30pm 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.
Full-day: 8:30am – 4:30pm 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.
Full-day: 8:30am – 4:30pm PDT
Generative AI has taken the world by storm, scaling machine learning to viably generate the written word, images, music, speech, video, and more. To the public, it is by far the most visible deployment of machine learning. To futurists, it is the most human-like. And to industry leaders, it has the widest, most untapped range of potential use cases.
In this workshop, participants will get an introduction to generative AI and its concepts and techniques. The workshop will cover different techniques for image, text, and 3D object generation, and so forth. Participants will also learn how prompts can be used to guide and generate output from generative AI models. Real-world applications of generative AI will be discussed, including image and video synthesis, text generation, and data augmentation. Ethical considerations when working with generative AI, including data privacy, bias, and fairness, will also be covered. Hands-on exercises will provide participants with practical experience using generative AI tools and techniques. By the end of the workshop, participants will have a solid understanding of generative AI and how it can be applied in various domains.