Predictive Analytics World for Industry 4.0 2022
June 19-24, 2022 l Caesars Palace, Las Vegas
Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Workshops - Sunday, June 19th, 2022
Full-day: 8:30am – 4:30pm.
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.
Workshops - Monday, June 20th, 2022
Full-Day 8:30 am - 4:30pm
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).
Full-Day 8:30 am - 4:30pm
Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.
Full-Day 8:30 am - 4:30pm
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 21st, 2022
Nvidia's Siddha Ganju has gained a unique perspective on machine learning's cross-sector deployment. In her current role, she work's on a range of applications, from self-driving vehicles to healthcare, and she previously led NASA's Long-Period Comets team, applying ML to develop meteor detectors. Deep learning impacts the masses, so it demands mass, interdisciplinary collaboration. In this keynote session, Siddha will describe the very particular interdisciplinary effort -- driven by established joint directives -- required to successfully deploy deep learning across a variety of domains, including climate, planetary defense, healthcare, and self-driving cars.The format of this session will be a "fireside chat," with PAW Founder Eric Siegel interviewing Siddha in order to dig deep into the lessons she's learned.
Machine learning and robotics are dramatically shifting our industrial capabilities and are opening new doors to our functional understanding and ways to support the natural world. Together, these advances can enable something far beyond simply limiting our damage to the planet -- they create the possibility of building a new relationship to nature wherein our industrial footprint can be radically reduced and nature's capability to support itself and all life on Earth (including us!) can be amplified.
As the world of Machine Learning (ML) has advanced, the biggest challenge that still faces data science organizations is the need for insightful, valuable, predictive attributes, aka “features” that can be applied to ML models. The process of building features is so tedious and costly that the “feature store” was invented to make re-building features a thing of the past.
The problem is that traditional means of building features to feed feature stores have been manual, labor-intensive efforts that involve data engineers, subject matter experts, data scientists, and your IT department. But what if there was a faster and more scalable way? Join dotData’s VP of Data Science, Dr. Aaron Cheng as he presents the concept of the automated Feature Factory and see how your organization can take a process that today takes months, and do it in a few days.
Predictive maintenance (PdM) has made significant strides in recent years and represents the strongest solution to the persistent manufacturing challenge of unplanned downtime. While many manufacturers understand the benefits that an IoT-based PdM solution can provide, the majority of them are still struggling to successfully implement these solutions. Markus Larsson, head of Predictive Maintenance at PARC talks about how manufacturers can successfully deploy and manage PdM solutions and put themselves on the path to zero unplanned downtime.
Additive manufacturing aka 3D Printing is fast becoming a viable option for final part manufacturing as material choices grow along with advancements in core printing technology. One of the key challenges is the ability to produce high quality parts with repeatability and to address this challenge there is a need for an automated part quality monitoring and prediction system.
In this session we present a system we developed that is designed to aid process engineers from designing a process, conducting Design Of Experiments(DOE), verifying the process, to deploying and monitoring the process in real/near real time.
Real/near real time monitoring of a process is based on the concept of a process digital twin. The process digital twin is a collection of machine learning models (supervised and unsupervised) working in unison to detect anomalies and generate alerts during the manufacturing process. Each of these models that make up the digital twin are developed based on the data generated (process parameters, telemetry and metrology) during the DOE and process verification phases and may be targeted at a subsystem. The models are continuously updated based on new data collected during the production phases. The system has been deployed internally for testing purposes.
Sales forecasting is a key process in defining a realistic revenue target for public and private companies and it’s a backbone of short term and long-term strategic planning. Despite the rapid growth of AI application across many business processes, Sales forecasting to a large extent has still been driven by human intelligence, a time-consuming effort with high likelihood of human error and significant inaccuracy. Many prior efforts in developing large-scale sales forecasting engines have not been successful mainly due to the lack of clear definition on what machines can tackle vs human. This presentation demonstrates the strategy and process that led to the development of a large scale AI driven sales forecasting engine in practice that impacted many business processes including Revenue Recognition, Commercial Planning, Product Marketing, Supply Chain, and Strategic Planning.
It takes 10-13 years to design, manufacture and deliver a new aerospace product, which can inhibit growth for the industry as a whole. With a strained global supply chain, it’s more important than ever to make design and manufacturing processes more efficient in order to keep pace with forecasted demand in commercial travel. In this session, Joakim Soederberg, Head of Data at Acubed, the Silicon Valley Innovation Center of Airbus, will discuss how to apply model-based engineering and digital technologies to manufacturing processes in order to reduce lead times, production costs and improve workflows dynamically.
The majority of organizations have AI as a top company initiative, yet only 1% of models created today have their desired business impact. In this session, we’ll unpack the 3 most common roadblocks that cause AI projects to stall or fail completely. Then, of course, discuss the best way to overcome them by leveraging resources you already have.
We’ll share how a global sustainability-focused paper manufacturer found a secret weapon to help scale the impact of a small team of only 3 data scientists to more than 200 non-coders completing advanced AI projects in less than 6 months.
RapidMiner was started by PhD data scientists who understood that the power of AI shouldn’t be reserved for… PhD data scientists. RapidMiner is a no-code data science platform that can enable anyone in your organization to complete AI/ML projects—from making sense of your data to building models and AI-powered apps to drive better decision-making.
Johnson Controls began as a thermometer manufacturer in 1883 and operates, now, as a global leader in building controls, equipment manufacturer and service provider. In the middle of a digital transformation, the firm strives to use the best practices of Predictive Analytics and Machine Learning to improve operational performance in its Global Services business. This presentation will discuss the journey of the company's Global Services business, from its analytics greenfield origin through the present, detailing all aspects of building the infrastructure necessary to solve problems using Machine Learning and Predictive Analytics. Follow along as we discuss the real-world, complicated steps necessary to predict customer CHURN using legacy industrial data from disparate, non-curated systems.
With python, it is easier than ever to retrieve very targeted data from massive document repositories, apply NLP to create curated datasets, and then mine that text data for domain-specific insights. This case study will discuss a simple 5-step process for extracting information from a U.S. government database for regulatory compliance. The business goal is to identify the questions that regulators ask relative to certain operating conditions, and how peer companies in the industry have responded. These methods would also be useful for other use cases such as analyzing work orders, maintenance logs, and other text data sources relating to plant operations.
Is your team facing challenges getting ML models into deployment? Bring your obstacles and roadblocks and in this session, we'll help you bust through them. Our panel of experts will share practical advice and actionable takeaways. Is your organization crushing it when it comes to deploying models? Join the discussion and help your peers who may be a little less fortunate.
Long short term memory neural networks (LSTMs) originally rose to prominence in natural language processing, but have also shown value in time series. One drawback of LSTMs, and neural networks more generally, is that it can be difficult to derive effective confidence intervals. Two primary methods for calculating confidence intervals in LSTMs have been proposed: dropout-based simulation and estimating probability distributions.
In this case study we look at dropout-based simulation, which we found to be effective and more flexible than distributional estimation. However, added flexibility came at the cost of elevated computational burden.
Predictive Analytics World for Industry 4.0 - Las Vegas - Day 2 - Wednesday, June 22nd, 2022
The UPS Smart Logistics network is a framework that continually incorporates the latest technology trends to serve customers better and more efficiently. Today it connects all the components of the transportation value chain by integrating Operations, Technology, Data and Optimization. Among many technological innovations, machine learning plays a critical role in the planning and execution of our integrated transportation network. In this talk, we will give an overview on applying predictive analytics to different phases of the network planning. The self-learning Demand Management model will be spotlighted with technical details and business impact. As for the connection to day-to-day operation, we will share our experience on deploying machine learning models to automate the key planning and execution decisions. At the end, we will also share our vision of transforming our self-learning network to a smarter self-healing network.
Our latest research shows most models fail to deploy. Machine learning's operationalization -- the model's change to existing processes in order to improve them -- takes a lot more planning, socialization, and change-management efforts than most data scientists ever begin to realize. The problem is more in leadership than in technology; no technical solution such as MLops addresses the fundamental root of the problem. Without deployment, ML does not achieve value.
This industrywide crisis stems from a lack of proper ML leadership. The great potential of ML is intact -- the value proposition is solid and the core tech, research, and analytical results are legit. And it isn't a flop -- many ML projects succeed, even if only a minority. In this talk, Machine Learning Week founder and bestselling author of "Predictive Analytics" Eric Siegel will outline the required ML leadership practice. It ain't rocket science, but it's rarely well understood and thoroughly executed.
Value chains have been evaluated for decades. In this era of digital transformation, understanding the Data Science Value Chain is critical, but seldom is it examined as a system, nor re its component parts subject to systematic study. In this session, it will be shown how ML exists as a component within the value chain along with data acquisition, cleansing, formatting and accessibility. A conceptual case study aggregated from several non-specific sources shows the path from good data to the benefits of ML over traditional methods such as Designed Experiments. An overview of the 4.0 culture is integrated for a broader view of the benefit of ML within the value chain. Digital transformation's effects on the value chain are also integrated within the 4.0 culture. This presentation will highlight the myth that "everything data belongs to IT" by showing management and non-IT professionals the need for more knowledge about the data science value chain and where they fit within its constructs. Proposing a collaborative activity among the non-IT parts of the organization and an analytics maturity model expands where and how ML benefits decision making throughout the organization. The collaborative process also enhances communication of ML results to assist management in seeing beyond IT as a sole resource.
Hitachi and Arviem are delivering insights from 10+ years of data in the marine cargo industry. Traditionally, businesses have had limited visibility into the condition of their shipments while they are transported. By using ML and IoT data, the team helps reduce losses and identify root causes of outcomes. Methods range from clustering to weak supervision. We answer questions such as: Is fragile cargo likely damaged? What kind of packaging would have prevented damage? Is a particular shipment likely to develop mold? In addition to manufacturers and shippers, insurers can also fine-tune underwriting models and improve claims processing.
Water is fundamental to an effective society. Individuals and organizations throughout society, including drinking water and wastewater systems, possess multifaceted relationships to water. Broadly, society seeks safe and abundant supplies of water while avoiding modern challenges of aging infrastructure, emerging contaminants, cybersecurity threats, legal compliance, customer satisfaction, climate change, ESG commitments, among other matters. Machine learning (ML) applications continue to offer meaningful solutions for drinking water and wastewater systems and other organizations with a relationship to water. The panel articulates the state of ML and its future implications in meaningfully addressing the challenges facing the water sector.
This case study presents how the biggest building material company worldwide increased sales and reduced inventory by predicting 80% of its stockouts a week ahead using ML. The Company faced high stockout levels in Brazil due to limited warehouse space against high safety stock requirements affected by COVID. No historic data would explain the new pandemic-influenced demand pattern. The Company gained operational flexibility by creating a reaction process to mitigate out-of-stock risks using stockout’s forecasts in a pragmatic, simple to run and highly comprehensible approach.A scalable data structure and pipeline was set up to give full operational visibility of the previous two years, such as daily stock levels, transit stocks, production plans, schedules, demand forecasts/actuals, committed order books, stockouts history and more. The team trained a gradient-boosting algorithm that uses tree-based learning for the supervised problem of classifying whether there will be a following week stockout for a given warehouse-product combination. To have consistent results across regions and products the algorithm was trained with different time horizons, comparing accuracy and identifying new variables to explain deviations. After two months, the initiative released an 80% accuracy algorithm and F1 over 0.7 for top 5 warehouses in volume and 87 products.
The rate of adoption for AutoML and MLOps solutions is incredible. Despite all of the great work being done to operationalize ML across industry there are two areas which still require custom work: feature engineering and product integration. The AutoML we run is only as good as the data it has to learn from. We'll be discussing a Spark based approach to automating the feature engineering portion of any MLOps solution. The result is an abstracted, extensible solution for the feature engineering portion of your AutoML or MLOps solution.
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 23rd, 2022
Full-Day 8:30 am - 4:30pm
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 8:30 am - 4:30pm
This one day workshop reviews major big data success stories that have transformed businesses and created new markets.
Full-Day 8:30 am - 4:30pm
This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting.
3 hour workshop: 5:30-8:30pm
This 3 hour workshop launches your tenure as a user of R, the well-known open-source platform for data analysis.
Workshops - Friday, June 24th, 2022
Full-Day 8:30 am - 4:30pm
Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.