Agenda
Predictive Analytics World Climate 2021
May 24-28, 2021 – Livestreamed
Session Levels:
Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Workshops - Wednesday, May 19th, 2021
Full-day: 7:15am – 2:30pm PDT
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).
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.
Full-day: 8:00am – 3:00pm PDT
This one day workshop reviews major big data success stories that have transformed businesses and created new markets.
Workshops - Thursday, May 20th, 2021
Full-day: 8:00am – 3:00pm PDT
This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting.
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.
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.
Full-day: 8:30am – 3:30pm PDT
Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists.
Workshops - Friday, May 21st, 2021
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.
Predictive Analytics World for Climate - Virtual - Day 1 - Monday, May 24th, 2021
Accurate, timely estimation of carbon emissions is critical for businesses and governments to take action on climate change. The Climate TRACE coalition aims to furnish these estimates for all major sources of emissions globally, on a near real-time basis. In this session we’ll give an overview of our work on estimating emissions from one of the most important sources of greenhouse gases, power generation from coal. The approach focuses upon detecting plumes using a variety of approaches, including multi instance deep learning.
The energy landscape is going through a drastic transformation. We are moving away from centralized power plants to more distributed energy resources such as solar, electric vehicles. This transformation makes it very difficult for the grid to handle and manage. Through this session we would explore how using data science and artificial intelligence, industries can optimize their energy usage while aiming to reduce costs and meet their sustainability goals. We'd be delving deeper into some of the forecasting and predictive analytics techniques we have created and measuring their impact. Also, we would share insights from real-life use cases of our customers and how AI has helped them monitor, control, and optimize their energy assets.
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.
Predictive Analytics World for Climate - Virtual - Day 2 - Tuesday, May 25th, 2021
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.
As demand for carbon credits accelerates, there is an immense challenge in scaling the supply of carbon offsets. It’s hard to create credits that are additional, non-leaky, and durable, and it’s impossible for all but the largest landowners to participate in carbon programs. Over the last 10 years, SilviaTerra has built technology that generates comprehensive forest inventories of unprecedented resolution and scale, enabling measurement and payment for a comprehensive set of beneficial outcomes across the landscape. This new market is making carbon and other types of natural capital work for all landowners - for every acre, every value, every year.
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.
Predictive Analytics World for Climate - Virtual - Day 3 - Wednesday, May 26th, 2021
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.
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.
We give an overview of recent developments in physics-informed AI and big data that are transforming the prediction of climate and weather in applications ranging from climate risk modeling for insurance to real-time forecasting for energy. Traditional climate and weather models require computationally expensive simulation of physical laws on supercomputers with hours to days of processing time and have limited capacity to incorporate ground-truth data sources. The development of cloud-based AI workflows based on deep neural networks provides an alternative approach to develop physical emulators of climate and weather processes that are highly scalable and natively tuned to utilize the petabytes of remote-sensing, ground-based and numerical simulation data from Earth observation that are generated daily. We present work that we are doing at Terrafuse AI, a startup out of Berkeley National Lab, to develop an AI-native climate risk and forecasting platform for problems ranging from high-resolution mapping of wildfire risk in California to real-time wind forecasting for aviation and renewable energy.
Predictive Analytics World for Climate - Virtual - Day 4 - Thursday, May 27th, 2021
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.
Methane, the primary component of natural gas, is responsible for 15% of global warming. Our mission of finding and stopping greenhouse gas emissions at huge scale is a critical step towards controlling climate change, but it also presents unique challenges. And, as a small startup, navigating the trade-offs between speed, accuracy, and cost in our data pipeline can often be the difference between survival and failure. In this talk, we will examine the difficulties ML pipeline design in cases where information, time, and money are constrained, and how to do so while hiding the sausage-making from our customers, who just want to know where their equipment is leaking, and want to know fast. By using a lean, iterative approach that involves input from every department, including engineering, operations, and business development, we stay focused on creating analytics that maximize value while reducing risk to the company.
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.
The global recycling industry has been in economic crisis over the last few years as China and other international importers of recyclables enacted stricter requirements on the purity of recycled materials. COVID-19 exacerbated the recycling crisis, forcing many businesses to suspend operations due to concerns for worker safety. At the same time, the pandemic increased demand for high-quality recycled feedstock to overcome supply chain interruptions and shifts in raw material availability. AMP’s technology applies computer vision and deep learning to identify and differentiate recyclables found in the waste stream by color, size, shape, opacity, consumer brand, and more, storing data about each item it perceives. AMP’s AI can recognize and recover material as small as a bottlecap and as unique as a Keurig coffee pod from complex, mixed material streams of plastics, cardboard, paper, cans, cartons, and many other container and packaging types reclaimed for raw material processing for the global supply chain. Learn how AMP is helping the industry overcome the crisis by modernizing recycling—keeping recycling businesses open, ensuring worker safety, increasing productivity, improving bale purity, overcoming labor shortages, lowering the costs to recycle, diverting materials from landfill, and increasing overall rates of recycling.
Predictive Analytics World for Climate - Virtual - Day 5 - Friday, May 28th, 2021
Climate change is increasing the frequency and severity of natural disasters. Natural catastrophes impact all critical infrastructures, and their resilience is essential for businesses and cities to operate effectively and safely. At One Concern, we combine machine learning and hazard modeling along with ML operational tools to better model the impacts of natural disasters on these critical infrastructures. By taking advantage of modeling, we can understand these potential impacts sooner to plan for and mitigate them. This helps to make our communities more resilient. This session will cover how One Concern applies Machine Learning algorithms to Natural Disaster Modeling.
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.
About 30-40% of food produced worldwide is wasted. This represents a $165B loss to the US economy and poses major environmental problems: it is estimated that food waste contributes to up to 25% of all greenhouse gas emissions. This session explores how artificial intelligence can be used to automate decisions across the food supply chain in order to reduce waste and increase the quality and affordability of food. We focus our attention on supermarkets — combined with downstream consumer waste, these contribute to 40% of total US food losses — and we describe an intelligent decision support system for supermarket operators that optimizes purchasing decisions and minimizes losses. The core of our system is a model-based reinforcement learning engine for perishable inventory management. Our system is currently deployed across 220 supermarkets in the US (handling ~2% of US produce volume) and has led to waste reductions of up to 50%. We hope that this talk will bring the food waste problem to the attention of the machine learning community.