Sep 9–12, 2019
 
230 A
230 B
Add Building reinforcement learning models and AI applications with Ray to your personal schedule
1:30pm Building reinforcement learning models and AI applications with Ray Robert Nishihara (University of California, Berkeley), Philipp Moritz (University of California, Berkeley), Ion Stoica (University of California, Berkeley)
230 C
Add Bringing AI into the enterprise to your personal schedule
9:00am Bringing AI into the enterprise Kristian Hammond (Northwestern Computer Science)
LL21 A/B
Add Build a self-driving car without a car: ML problem-solving with a game engine to your personal schedule
9:00am Build a self-driving car without a car: ML problem-solving with a game engine Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania)
Add Deep learning at scale: Tools and solutions to your personal schedule
1:30pm Deep learning at scale: Tools and solutions Angela Wu (Determined AI), Sidney Wijngaarde (Determined AI), Shiyuan Zhu (Determined AI), Vishnu Mohan (Determined AI)
LL21 C/D
Add Getting started with PyTorch to your personal schedule
1:30pm Getting started with PyTorch Mo Patel (Independent)
LL21 E/F
Add Using Keras to classify text with LSTMs and other ML techniques to your personal schedule
9:00am Using Keras to classify text with LSTMs and other ML techniques Lukas Biewald (Weights & Biases)
Add Putting cutting-edge modern NLP into practice to your personal schedule
1:30pm Putting cutting-edge modern NLP into practice Joel Grus (Allen Institute for Artificial Intelligence)
231
Add Getting started with Kubeflow to your personal schedule
9:00am Getting started with Kubeflow Skyler Thomas (MapR)
Add Hands-on machine learning with Kafka-based streaming pipelines to your personal schedule
1:30pm Hands-on machine learning with Kafka-based streaming pipelines Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend)
Almaden Ballroom (Hilton)
Add Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark to your personal schedule
9:00am Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark Jason Dai (Intel), Yuhao Yang (Intel), Jiao(Jennie) Wang (Intel), Guoqiong Song (Intel)
Add Design thinking for AI to your personal schedule
1:30pm Design thinking for AI Chris Butler (IPsoft)
111
Add AI for managers (Day 2) to your personal schedule
9:00am TRAINING AI for managers (Day 2)
112
113
114
Santa Clara Room (Hilton)
Market (Hilton)
10:30am Morning Break | Room: Outside meeting rooms
3:00pm Afternoon Break | Room: Outside meeting rooms
8:00am Morning Coffee | Room: Level 2 West Lobby
Add AI Dine-Around to your personal schedule
7:00pm AI Dine-Around | Room: Various Locations
12:30pm Lunch sponsored by Intel | Room: Executive Ballroom 210
Add Emerging AI Pioneers Showcase to your personal schedule
5:00pm Emerging AI Pioneers Showcase | Room: Lower Level Foyer
1:30pm-7:30pm (6h)
AI in the Enterprise: The Intel® AI Builders Showcase Event
Join us during the O’Reilly Artificial Intelligence Conference to learn how to deploy enterprise AI solutions with Intel and its partner ecosystem. This event features offerings for a wide variety of industries and AI use cases.
9:00am-12:30pm (3h 30m) Implementing AI Design, Interfaces, and UX, Text, Language, and Speech
Going beyond FAQ assistants with machine learning and open source tools
Justina Petraityte (Rasa)
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.
1:30pm-5:00pm (3h 30m) Implementing AI Machine Learning, Reinforcement Learning
Building reinforcement learning models and AI applications with Ray
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.
9:00am-5:00pm (8h) AI Business Summit, Impact of AI on Business and Society
Bringing AI into the enterprise
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.
9:00am-12:30pm (3h 30m) Implementing AI Computer Vision, Machine Learning, Mobile Computing, IoT, Edge, Reinforcement Learning
Build a self-driving car without a car: ML problem-solving with a game engine
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.
1:30pm-5:00pm (3h 30m) Implementing AI, Models and Methods Deep Learning, Deep Learning tools, Hardware, Machine Learning
Deep learning at scale: Tools and solutions
Angela Wu (Determined AI), Sidney Wijngaarde (Determined AI), Shiyuan Zhu (Determined AI), Vishnu Mohan (Determined AI)
Success with DL requires more than just TensorFlow or PyTorch. Angela Wu, Sidney Wijngaarde, Shiyuan Zhu, and Vishnu Mohan detail practical problems faced by practitioners and the software tools and techniques you'll need to address the problems, including data prep, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, mobile and edge optimization, and more.
9:00am-12:30pm (3h 30m) AI Business Summit, Executive Briefing/Best Practices Machine Learning
Herding cats: Product management in the machine learning era
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.
1:30pm-5:00pm (3h 30m) Implementing AI Computer Vision, Deep Learning tools, Machine Learning
Getting started with PyTorch
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.
9:00am-12:30pm (3h 30m) Models and Methods Deep Learning, Deep Learning tools, Machine Learning, Text, Language, and Speech
Using Keras to classify text with LSTMs and other ML techniques
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.
1:30pm-5:00pm (3h 30m) Implementing AI Machine Learning, Text, Language, and Speech
Putting cutting-edge modern NLP into practice
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; 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.
9:00am-12:30pm (3h 30m) Implementing AI, Interacting with AI, Models and Methods Deep Learning tools, Machine Learning
Getting started with Kubeflow
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.
1:30pm-5:00pm (3h 30m) Implementing AI Machine Learning
Hands-on machine learning with Kafka-based streaming pipelines
Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend)
Boris Lublinsky and Chaoran Yu 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.
9:00am-12:30pm (3h 30m) Interacting with AI Computer Vision, Deep Learning tools, Machine Learning, Temporal data and time-series
Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark
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.
1:30pm-5:00pm (3h 30m) AI Business Summit, Impact of AI on Business and Society
Design thinking for AI
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.
9:00am-5:00pm (8h)
AI for managers (Day 2)
Nicholas Cifuentes-Goodbody leads you through a nontechnical overview of AI and data science. You’ll learn how to apply common techniques in organization and common pitfalls. You’ll pick up the language and develop a framework to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.
9:00am-5:00pm (8h) Deep Learning, Deep Learning tools, Machine Learning
Deep learning with PyTorch (Day 2)
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Get the knowledge you need to build deep learning models using real-world datasets and PyTorch with Rich Ott.
9:00am-5:00pm (8h)
Recommendation system using deep learning (Day 2)
Recommendation systems play a significant role—for users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system.
9:00am-5:00pm (8h) Deep Learning, Deep Learning tools, Machine Learning
Put deep learning to work: A practical introduction using Amazon Web Services (Day 2)
Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev detail a practical next step in DL learning with instructions, demos, and hands-on labs.
9:00am-5:00pm (8h) Deep Learning, Machine Learning, Text, Language, and Speech
Natural language processing with deep learning (Day 2)
Delip Rao and Brian McMahan explore natural language processing using a set of machine learning techniques known as deep learning. They walk you through neural network architectures and NLP tasks and teach you how to apply these architectures for those tasks.
9:00am-5:00pm (8h) Deep Learning, Deep Learning tools, Machine Learning
Deep learning with TensorFlow (Day 2)
The TensorFlow library provides computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll walks you through TensorFlow's capabilities in Python from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.
10:30am-11:00am (30m)
Break: Morning Break
3:00pm-3:30pm (30m)
Break: Afternoon Break
8:00am-9:00am (1h)
Break: Morning Coffee
7:00pm-9:00pm (2h)
AI Dine-Around
Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town.
12:30pm-1:30pm (1h)
Break: Lunch sponsored by Intel
5:00pm-7:00pm (2h)
Emerging AI Pioneers Showcase
If you're an AI pioneer, we’d love for you to participate in the Emerging AI Showcase at the O’Reilly AI Conference. Our team of investors, entrepreneurs, and industry experts will review all submissions and invite 10 finalists to present their technologies and tell their stories during the Emerging AI Showcase on Tuesday, September 10.

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