Fueling innovative software
July 15-18, 2019
Portland, OR

Schedule: Incorporating Artificial Intelligence sessions

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1:30pm5:00pm Monday, July 15, 2019
Location: Portland 251
Secondary topics:  AI Enhanced
Get hands-on experience with Justina Petraityte in developing intelligent AI assistants based entirely on machine learning and using only open source tools—Rasa NLU and Rasa Core. You'll learn the fundamentals of conversational AI and the best practices of developing AI assistants that scale and learn from real conversational data. Read more.
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1:30pm5:00pm Monday, July 15, 2019
Location: Portland 300
Secondary topics:  AI Enhanced
Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee)
Are you a scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test their AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered AI problem solving using game engines? No? We'll teach you how. Read more.
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9:00am12:30pm Tuesday, July 16, 2019
Location: Portland 255
Secondary topics:  AI Enhanced
Grishma Jena (IBM)
Grishma Jena introduces natural language processing using Python, where you start off with textual data and learn how to process it to derive useful insights that can be used in real-world applications. Read more.
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1:30pm5:00pm Tuesday, July 16, 2019
Location: Portland 255
Secondary topics:  AI Enhanced
Amy Unruh (Google)
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Amy Unruh leads a hands-on introduction to Kubeflow and Kubeflow Pipelines for ML, both from the command line and from a notebook. Read more.
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1:30pm5:00pm Tuesday, July 16, 2019
Location: Portland 300
Secondary topics:  AI Enhanced
Given a growing demand for fairness, accountability, and transparency from machine learning (ML) systems, Animesh Singh, Svetlana Levitan, and Tommy Li examine open source projects to build an ML pipeline that is open, secure and fair, and that fully integrates into the AI lifecycle, and you'll learn about AI Fairness 360 (AIF360) and Adversarial Robustness Toolbox (ART), among others. Read more.
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11:00am11:40am Wednesday, July 17, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Sara Robinson (Google)
Do you want to build a machine learning model but aren't sure where to start? Sara Robinson starts with an empty notebook and ends with a simple neural network, coded from start to finish, and she demonstrates how to train and serve the model on Google Cloud Platform. Read more.
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11:50am12:30pm Wednesday, July 17, 2019
Location: C123-124
Secondary topics:  AI Enhanced
With increasing regularity we see stories in the news about machine learning algorithms causing real-world harm. People's lives and livelihoods are affected by the decisions made by machines. Maureen McElaney examines how bias can take root in machine learning algorithms and ways to overcome it. Read more.
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1:45pm2:25pm Wednesday, July 17, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Holden Karau (Google), Trevor Grant (IBM)
In this talk we will show how to build a machine learning model and set up serving across clouds with Kubeflow. Read more.
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2:35pm3:15pm Wednesday, July 17, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Machine Learning has revolutionized how we drive, make decisions, and even communicate with each other and our computers ... but the way we code hasn't significantly changed since the seventies. It's time to make that change! Read more.
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4:15pm4:55pm Wednesday, July 17, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Ellen Korbes (garden.io)
Studying neural networks is a surefire way to end up fighting more math than you can shake a stick at. Wish you could learn about the likes of gradient descent and backpropagation in a language you actually understand—like Go? Then this one is for you. Code, not math! Algorithms, not logarithms! Read more.
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5:05pm5:45pm Wednesday, July 17, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Alasdair Allan (Babilim Light Industries)
The future of machine learning is on the edge and on small embedded devices that can run for a year or more on a single coin cell battery. Using deep learning can be very energy-efficient, and allows us to make sense of sensor data in real time. This talk shows you how. Read more.
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11:00am11:40am Thursday, July 18, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Julien Simon (AWS)
How to build machine learning inference pipelines using Open Source libraries, and how to scale them on AWS. Read more.
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11:50am12:30pm Thursday, July 18, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Angie Jones (Applitools)
An engaging tale describing the importance of verifying the ever-growing applications of machine learning and overcoming the challenges involved in doing so. Read more.
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1:45pm2:25pm Thursday, July 18, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Sam Charrington (This Week in Machine Learning & AI)
Building and deploying machine learning models at scale requires efficient platform technologies for data, experiment, and model management; at this session we'll review key platform requirements and the open source technologies that address them. Read more.
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2:35pm3:15pm Thursday, July 18, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Chris Thalinger (Twitter)
Running Twitter services on Graal has been very successful and saved Twitter a lot of money on datacenter cost but we can save even more using our Machine Learning framework called Autotune to tune Graal inlining parameters. Read more.
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4:15pm4:55pm Thursday, July 18, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Anais Jackie Dotis (InfluxData)
I'll show you how to use K-Means for time series anomaly detection and when it makes sense to use Machine Learning. Read more.
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5:05pm5:45pm Thursday, July 18, 2019
Location: C123-124
Secondary topics:  AI Enhanced
Tania Allard (Microsoft)
Machine learning in production is different to ML in R&D environment. This session will present a number of techniques to test your ML quality and decay in both your R&D and production environments appropriately. We will present examples of issues commonly encountered in the ML area and how to test and monitor your data, model development and infrastructure. Read more.