Sep 9–12, 2019

Tutorials

These expert-led presentations on Tuesday, September 10 give you a chance to dive deep into the subject matter. Please note: to attend tutorials, you must be registered for a Gold or Silver pass; does not include access to training courses on Monday or Tuesday.

Tuesday, September 10

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9:00am12:30pm
Location: LL21 A/B
Secondary topics:  Design, Interfaces, and UX, Text, Language, and Speech
AI assistants are among the most in demand topics in tech. Get hands-on experience with Justina Petraityte as you develop 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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9:00am12:30pm
Location: LL21 C/D
Secondary topics:  Deep Learning tools, Machine Learning
Skyler Thomas (MapR)
The popular open source Kubeflow project is one of the best ways to start doing machine learning and AI on top of Kubernetes. However, Kubeflow is a huge project with dozens of large complex components. Skyler Thomas dives into the Kubeflow components and how they interact with Kubernetes. He explores the machine learning lifecycle from model training to model serving. Read more.
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9:00am12:30pm
Location: 230 B
Secondary topics:  Machine Learning
Ira Cohen (Anodot)
While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores what it takes to manage ML-based products, the cycle of developing ML-based capabilities (or entire products), and the role of the (product) manager in each step of the cycle. Read more.
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9:00am12:30pm
Location: Almaden Ballroom
Secondary topics:  Computer Vision, Deep Learning tools, Machine Learning, Temporal data and time-series
Jason Dai (Intel), Yuhao Yang (Intel), Jiao(Jennie) Wang (Intel), Guoqiong Song (Intel)
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLS Listings, the World Bank, Baosight, and Midea/KUKA. Read more.
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9:00am5:00pm
Location: 230 C
Kristian Hammond (Northwestern Computer Science)
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making. Read more.
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9:00am12:30pm
Location: LL21 E/F
Secondary topics:  Deep Learning, Deep Learning tools, Machine Learning, Text, Language, and Speech
Lukas Biewald (Weights & Biases)
Join Lukas Biewald to build and deploy long short-term memories (LSTMs), grated recurrent units (GRUs), and other text classification techniques using Keras and scikit-learn. Read more.
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9:00am12:30pm
Location: 231
Secondary topics:  Computer Vision, Machine Learning, Mobile Computing, IoT, Edge, Reinforcement Learning
Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania)
Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Tim Nugent, and Mars Geldard teach you how to use solution-driven ML AI problem solving with a game engine. Read more.
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1:30pm5:00pm
Location: LL21 C/D
Secondary topics:  Machine Learning
Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
Get your hands dirty with Boris Lublinsky and Dean Wampler as they examine ML use in streaming data pipelines, how to do periodic model retraining, and low-latency scoring in live streams. Learn about Kafka as the data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, metadata tracking, and more. Read more.
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1:30pm5:00pm
Location: 230 B
Chris Butler (IPsoft)
Purpose, a well-defined problem, and trust are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment. Read more.
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1:30pm5:00pm
Location: Almaden Ballroom
Secondary topics:  Computer Vision, Deep Learning tools, Machine Learning
Mo Patel (Independent)
PyTorch captured the minds of ML researchers and developers upon its arrival. Now it's matured into a production-ready ML framework with use cases and applications. Mo Patel explores the PyTorch lifecycle via hands-on examples such as image and text classification and linear modeling. You'll cover other aspects of ML such as transfer learning, data modeling, and deploying to production in labs. Read more.
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1:30pm5:00pm
Location: 231
Secondary topics:  Machine Learning, Reinforcement Learning
Robert Nishihara (University of California, Berkeley), Philipp Moritz (University of California, Berkeley), Ion Stoica (University of California, Berkeley)
Building AI applications is hard, and building the next generation of AI applications, such as online and reinforcement learning (RL), is more challenging. Robert Nishihara, Philipp Moritz, and Ion Stoica lead a deep dive into Ray—a general-purpose framework for programming your cluster—its API, and system architecture and examine application examples, including state-of-the-art algorithms. Read more.
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1:30pm5:00pm
Location: LL21 A/B
Secondary topics:  Deep Learning, Deep Learning tools, Hardware, Machine Learning
Neil Conway (Determined AI), Yoav Zimmerman (Determined AI)
Success with DL requires more than just TensorFlow or Keras. Neil Conway and Yoav Zimmerman detail a range of practical problems faced by DL practitioners and the software tools and techniques you'll need to address the problems, including data prep and augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile and edge optimization. Read more.
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1:30pm5:00pm
Location: LL21 E/F
Secondary topics:  Machine Learning, Text, Language, and Speech
Joel Grus (Allen Institute for Artificial Intelligence)
AllenNLP is a PyTorch-based library designed to make it easy to do high-quality research in natural language processing (NLP). Joel Grus explains what modern neural NLP looks like, and you'll get your hands dirty training some models, writing some code, and learning how you can apply these techniques to your own datasets and problems. Read more.

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