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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 five 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 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
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 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.
Join Kian Katanforoosh, CEO and Founder of Workera, as he explores the profound impact of generative AI on the workforce and the evolution of personalized learning. With his rich experience in AI education and having taught AI to over 4 million people with Prof. Andrew Ng as founding member of DeepLearning.AI, Kian's insights are uniquely informed and forward-thinking.
In this keynote, Kian will unravel how generative AI is reshaping learning, emphasizing the pivotal role of skills data in actualizing personalized learning. He will discuss the harnessing of this data to tailor learning experiences to individual needs, track progress, identify improvement areas, and improve workforce management.
Drawing from his experiences as a founding member of DeepLearning.AI and the co-creator of the popular Stanford Deep Learning Class, Kian will share his vision for a future where learning is as unique as we are. Attend this session for a deep dive into the convergence of AI, personalized learning, and workforce transformation.
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.
In this session, Juan Acevedo, a machine learning architect at Google, will discuss how organizations can leverage Google Cloud's generative AI products to bring value to their businesses in a secure environment and responsibly. Juan will cover the following topics: What can you do right now with Google Cloud technology Responsible generative AI This session is for those who are interested in learning more about generative AI and how it can be used to improve their businesses.

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.
Machine learning (ML) is a rapidly growing field that opens a lot of opportunities for transformation workflows for many users. This presentation will introduce how to enhance user productivity using Machine Learning and replace repetitive manual actions with intellectual ML suggestions. This talk will cover the following:
- Developing a universal ML workflow to process any type of users data (image and text)
- Instrumentation and feedback collection that will help drive future improvements by using incremental learning techniques
- Best practices in applying ML for optimizing the user experience working with Autodesk product
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.
Graph data structures provide a versatile and extensible data structure to represent arbitrary data. Data entities and their associated relations fit nicely into graph data structures. We will discuss GraphReduce, an abstraction layer for computing features over large graphs of data entities. This talk will outline the complexity of feature engineering from raw entity-level data, the reduction in complexity that comes with composable compute graphs, and an example of the working solution. We will also discuss a case study of the impact on a logistics & supply chain machine learning problem. If you work on large scale MLOps projects, this talk may be of interest.
How to Attract and Recruit Analytics Talent from Top Universities (especially if you're not a tech giant)Competition for top new analytics talent is fierce. While tech and other corporate giants are indeed vacuuming up new grads from top schools, not all great students can or want to go that route. The challenge is to put yourself in a position to attract and land them. It can be done, even if you're not a well-known brand. In this session, you will learn what works from a leader of the analytics program ranked #2 in the world by QS for the past three years. Even if you are from a giant firm, you're still competing for talent. You will come away with ideas to help you gain an advantage!
Predictive Analytics World for Industry 4.0 - Las Vegas - Day 2 - Wednesday, June 21st, 2023
Conference Chair Steven Ramirez will welcome you to day 2
Recent industry surveys have shown that up to 80% of ML models do not get deployed. There are roadblocks at every stage of the ML model development lifecycle. Is it possible we are going about this all wrong?
In this session, we will explore a series of case studies to drill down on the roadblocks to ML deployment. And along the way, identify the pieces you must have in place to improve your chances of deployment success.
This session will be interactive. If you’re ready to discuss your deployment challenges, you’ll be able to tap the wisdom of your peers.
Rexer Analytics began surveying data scientists in 2007. This year's Data Science Survey is a collaboration with "Predictive Analytics" author and conference series founder Eric Siegel. In this session, Dr. Rexer will present preliminary results from this year's survey. Topics will include algorithm choices, data science job satisfaction trends, deep learning, model deployment, and deployment challenges.
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.
Industrial manufacturing has gained largely from artificial intelligence (AI). Particularly, generative design has leveraged cloud computing and AI to allow fine-grained control over manufacturing processes, loads, and constraints. On one hand, AI-powered solutions simulate complex geometries with customizable material properties. On the other, AI has enabled control over processes without compromising design safety and with significant material savings. Additionally, the foundation models in computer vision and natural language have opened the doors to limitless possibilities.
Delivering a product that needs to meet global high-quality standards requires controlled design and manufacture, navigating the often confusing logistical supply chain, and customer-driven time-to-market deadlines. According to Forbes, unplanned downtime costs industrial manufacturers $50 billion a year. As manufacturing companies scale AI initiatives, the struggle remains to align their operational needs with AI capabilities.
The heavy reliance on limited or siloed data can lead to severe compromises on accuracy and the value of insights driven by AI systems. Moreover, with the increasing complexity of the product and strict quality-control standards, fast-moving production companies lag behind in being able to explain AI models. This inhibits the visibility into patterns that can lead to potential problems or failures that result in unplanned outages and low production efficiency.
Join a session with Ayush Patel, Co-founder at Twelvefold as he takes you through the AI-enabled generative design landscape and its challenges. Explore key strategies to maintain scalable yet reliable AI solutions while achieving significant ROI from manufacturing operations.
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.