Sep 9–12, 2019
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Schedule: Implementing AI sessions
9:00am–12:30pm Tuesday, September 10, 2019
Location: 230 B
Secondary topics:
Design, Interfaces, and UX,
Text, Language, and Speech
Justina Petraityte (Rasa)
Average rating:
(5.00, 1 rating)
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.
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9:00am–12:30pm Tuesday, September 10, 2019
Location: LL21 A/B
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)
Average rating:
(4.89, 9 ratings)
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.
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9:00am–12:30pm Tuesday, September 10, 2019
Location: 231
Secondary topics:
Deep Learning tools,
Machine Learning
Skyler Thomas (MapR)
Average rating:
(4.25, 4 ratings)
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.
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1:30pm–5:00pm Tuesday, September 10, 2019
Location: 230 B
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.
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1:30pm–5:00pm Tuesday, September 10, 2019
Location: 231
Secondary topics:
Machine Learning
Average rating:
(5.00, 1 rating)
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.
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1:30pm–5:00pm Tuesday, September 10, 2019
Location: LL21 A/B
Secondary topics:
Deep Learning,
Deep Learning tools,
Hardware,
Machine Learning
Angela Wu (Determined AI),
Sidney Wijngaarde (Determined AI),
Shiyuan Zhu (Determined AI),
Vishnu Mohan (Determined AI)
Average rating:
(4.67, 3 ratings)
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.
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1:30pm–5:00pm Tuesday, September 10, 2019
Location: LL21 C/D
Secondary topics:
Computer Vision,
Deep Learning tools,
Machine Learning
Mo Patel (Independent)
Average rating:
(2.67, 3 ratings)
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.
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1:30pm–5:00pm Tuesday, September 10, 2019
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; 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.
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9:35am–9:50am Wednesday, September 11, 2019
Location: Hall 2
Secondary topics:
Ethics, Security, and Privacy
Sarah Bird (Microsoft)
Average rating:
(4.00, 8 ratings)
Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Sarah Bird outlines her perspective on some of the major challenges in responsible AI development and examines promising new tools and technologies to help enable it in practice.
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11:05am–11:45am Wednesday, September 11, 2019
Location: 230 C
Secondary topics:
Deep Learning,
Deep Learning tools,
Hardware
Urs Köster (Cerebras Systems)
Average rating:
(5.00, 1 rating)
Long training times are the single biggest factor slowing down innovation in deep learning. Today's common approach of scaling large workloads out over many small processors is inefficient and requires extensive model tuning. Urs Köster explains why with increasing model and dataset sizes, new ideas are needed to reduce training times.
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11:05am–11:45am Wednesday, September 11, 2019
Location: LL21 C/D
Secondary topics:
Computer Vision,
Deep Learning,
Machine Learning
Josh Weisberg (Zillow Group)
Advances in AI and deep learning enable new technologies to mimic how the human brain interprets scenes, objects, and images, which has major implications for businesses that need to extract meaning from overwhelming quantities of unstructured data. Josh Weisberg walks you through how implementing computer vision based in deep neural networks allows machines to see images in an entirely new way.
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11:05am–11:45am Wednesday, September 11, 2019
Location: Expo Hall 3
Secondary topics:
Deep Learning,
Machine Learning,
Text, Language, and Speech
Huaixiu Zheng (Uber)
Average rating:
(4.33, 3 ratings)
Uber applies natural language processing (NLP) and conversational AI in a number of business domains. Huaixiu Zheng details how Uber applies deep learning in the domain of NLP and conversational AI. You'll learn how Uber implements AI solutions in a real-world environment, as well as cutting-edge research in end-to-end dialogue systems.
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11:05am–11:45am Wednesday, September 11, 2019
Location: 230 A
Secondary topics:
Machine Learning
Average rating:
(5.00, 1 rating)
Artificial intelligence has revolutionized the way we live, work, and play. With the help of AI, electronic payments have become more secure and more convenient for consumers globally—regardless of currency or form factor. Chiranjeet and Shubham explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure.
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11:55am–12:35pm Wednesday, September 11, 2019
Location: 230 C
Secondary topics:
Computer Vision,
Deep Learning,
Health and Medicine,
Machine Learning
Average rating:
(5.00, 1 rating)
Every NLP-based document-processing solution depends on converting documents or images to machine-readable text using an optical character recognition (OCR) solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how GANs can bring significant efficiencies by enhancing resolution and denoising scanned images.
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11:55am–12:35pm Wednesday, September 11, 2019
Location: Expo Hall 3
Secondary topics:
Deep Learning tools,
Design, Interfaces, and UX,
Text, Language, and Speech
Hagay Lupesko (Facebook)
Average rating:
(4.33, 6 ratings)
Hagay Lupesko explores AI-powered personalization at Facebook and the challenges and practical techniques it applied to overcome these challenges. You'll learn about deep learning-based personalization modeling, scalable training, and the accompanying system design approaches that are applied in practice.
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11:55am–12:35pm Wednesday, September 11, 2019
Location: 230 A
Secondary topics:
Deep Learning tools
Robert Crowe (Google)
Average rating:
(4.40, 5 ratings)
Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.
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11:55am–12:35pm Wednesday, September 11, 2019
Location: 230 B
Secondary topics:
Health and Medicine,
Machine Learning,
Text, Language, and Speech
Huaiyu Zhu (IBM Research - Almaden),
Dulce Ponceleon (IBM Research - Almaden),
Yunyao Li (IBM Research - Almaden)
Average rating:
(2.00, 1 rating)
Natural language understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages, but the questions of how to expand these resources without duplicating effort and if it's possible to develop language-agnostic NLU-dependent applications remains. Huaiyu Zhu, Dulce Ponceleon, and Yunyao Li believe the answer is yes.
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1:45pm–2:25pm Wednesday, September 11, 2019
Location: 230 C
Secondary topics:
Computer Vision,
Deep Learning,
Machine Learning
Akhilesh Kumar (Adobe)
Photographic defects such as noise, exposure, and blur can ruin the perfect shot. Adobe has developed a solution based on GAN that can identify the defective region in images and fix it. Akhilesh Kumar explores how this solution, which can also be applied to fix videos, is better than traditional algorithms and means you won't have to spend hours manually editing the images.
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1:45pm–2:25pm Wednesday, September 11, 2019
Location: Expo Hall 3
Secondary topics:
Deep Learning tools
Paige Bailey (Google)
Average rating:
(4.00, 1 rating)
TensorFlow 2.0 has landed. Paige Bailey walks you through TensorFlow (TF) 2.0's new features, usability enhancements, performance increases, and focus on developer productivity. You'll use the TF 2.0 migration tool to transition a model from TensorFlow 1.x to 2.0 and deploy an end-to-end open source machine learning model.
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1:45pm–2:25pm Wednesday, September 11, 2019
Location: 230 A
Secondary topics:
Computer Vision,
Machine Learning
Roshan Sumbaly (Facebook)
Average rating:
(4.50, 2 ratings)
There aren't many systems in the world that need to run hundreds of computer vision models (from classification to segmentation) on billions of visual entities (images, videos, 3-D) daily. Roshan Sumbaly walks you through the challenges faced while building such a platform and how, surprisingly, a lot of the answers were found in traditional software engineering best practices.
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2:35pm–3:15pm Wednesday, September 11, 2019
Location: Expo Hall 3
Secondary topics:
Deep Learning tools,
Hardware,
Machine Learning,
Text, Language, and Speech
Joseph Spisak (Facebook),
Hao Lu (Facebook)
Average rating:
(5.00, 1 rating)
Joseph Spisak and Hao Lu lead a deep dive into how PyTorch is being used to help accelerate the path from novel research to large-scale production deployment in computer vision, natural language processing, and machine translation at Facebook.
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2:35pm–3:15pm Wednesday, September 11, 2019
Location: 230 A
Secondary topics:
Deep Learning tools
Evan Sparks (Determined AI)
Evan Sparks walks you through the current gap between the AI haves (Google, Facebook, Amazon, and Microsoft) and the AI have-nots (the rest of the industry), from the perspective of software infrastructure for model development. You'll learn some of the opportunities for end-to-end system design to enable rapid iteration and scale in AI application development.
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4:00pm–4:40pm Wednesday, September 11, 2019
Location: Expo Hall 3
Secondary topics:
Text, Language, and Speech
Moshe Wasserblat (Intel)
Average rating:
(5.00, 1 rating)
Moshe Wasserblat demonstrates the challenges and reviews the latest AI solutions in deploying natural language processing (NLP) in commercial environments, specifically dealing with the small amount of data available for training and scaling across different domains.
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4:00pm–4:40pm Wednesday, September 11, 2019
Location: 230 A
Secondary topics:
Machine Learning,
Text, Language, and Speech
Ashish Bansal (Twitter)
Average rating:
(4.50, 2 ratings)
Twitter has amazing and unique content generated at an enormous velocity internationally in multiple languages. Ashish Bansal provides you with insight into the unique recommendation system challenges at Twitter’s scale and what makes this a fun and challenging task.
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4:50pm–5:30pm Wednesday, September 11, 2019
Location: LL21 C/D
Secondary topics:
Machine Learning,
Temporal data and time-series
Anuradha Gali (Uber)
There are 15 million trips a day on the Uber platform. Anu Gali walks you through how Uber leverages AI to automate its business model via its unique platform. You'll learn about technology that evolves based on current market insights and dynamically adjusts for the future. She shares best practices and the architecture that enables organizations like Uber to grow and scale rapidly.
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4:50pm–5:30pm Wednesday, September 11, 2019
Location: Expo Hall 3
Secondary topics:
Computer Vision,
Deep Learning tools,
Hardware,
Machine Learning,
Mobile Computing, IoT, Edge
Average rating:
(5.00, 2 ratings)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. Siddha Ganju and Meher Kasam examine optimizing deep neural nets to run efficiently on mobile devices.
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11:05am–11:45am Thursday, September 12, 2019
Location: 230 C
Secondary topics:
Data, Data Networks, Data Quality,
Health and Medicine
Alex Ratner (Snorkel)
Average rating:
(5.00, 3 ratings)
Alex Ratner explores programmatic approaches to building, managing, and modeling training data for machine learning (ML) using the open source framework Snorkel. Training data is increasingly one of the key bottlenecks to using modern ML, and Alex outlines recent systems and algorithmic and theoretical advances in building and managing training data for ML.
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11:05am–11:45am Thursday, September 12, 2019
Location: 230 A
Secondary topics:
Deep Learning tools,
Machine Learning
Join Danielle Dean, Mathew Salvaris, and Angus Taylor to learn best practices and reference architectures (which have been validated in real-world AI and ML projects for customers globally) for implementing AI. They detail lessons distilled from working with large global customers on AI and ML projects and the challenges that they overcame.
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11:55am–12:35pm Thursday, September 12, 2019
Location: LL21 E/F
Secondary topics:
Machine Learning,
Temporal data and time-series
Madhura Dudhgaonkar (Hiring) (Workday)
Average rating:
(4.25, 4 ratings)
Madhura Dudhgaonkar details lessons learned from productizing enterprise ML services across vision, language, recommendations, and anomaly detection over the last 5+ years. You'll walk away with an actionable framework to bootstrap and scale a machine learning function via a real product journey, involving deep learning that was productized in record speed, in spite of having no dataset.
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11:55am–12:35pm Thursday, September 12, 2019
Location: 230 C
Secondary topics:
Data, Data Networks, Data Quality,
Deep Learning,
Machine Learning,
Reinforcement Learning
Vijay Gabale (Infilect)
Beyond computer games and neural architecture search, practical applications of deep reinforcement learning (DRL) to improve classical classification or detection tasks are few and far between. Vijay Gabale outlines a technique and some experiences of applying DRL on improving the distribution input datasets to achieve state-of-the-art performance, specifically on object-detection tasks.
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11:55am–12:35pm Thursday, September 12, 2019
Location: LL21 C/D
Secondary topics:
Machine Learning,
Temporal data and time-series
Ting-Fang Yen (DataVisor)
Ting-Fang Yen details a monitor for production machine learning systems that handle billions of requests daily. The approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. You'll see new tools for detecting undesirable model behaviors early in large-scale online ML systems.
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1:45pm–2:25pm Thursday, September 12, 2019
Location: 230 C
Secondary topics:
Deep Learning tools,
Machine Learning
Shashank Prasanna (Amazon Web Services)
Average rating:
(3.80, 5 ratings)
Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker.
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1:45pm–2:25pm Thursday, September 12, 2019
Location: 230 A
Secondary topics:
Deep Learning,
Health and Medicine,
Machine Learning,
Text, Language, and Speech
Vijay Agneeswaran and Abhishek Kumar explore multilabel text classification problems, where multiple tags or categories have to be associated with a given text or documents. Multilabel text classification occurs in numerous real-world scenarios, for instance, in news categorization and bioinformatics (such as the gene classification problem).
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1:45pm–2:25pm Thursday, September 12, 2019
Location: 230 B
Secondary topics:
Machine Learning
Holden Karau (Independent)
Average rating:
(3.67, 3 ratings)
Modeling is easy—productizing models, less so. Distributed training? Forget about it. Say hello to Kubeflow with Holden Karau—a system that makes it easy for data scientists to containerize their models to train and serve on Kubernetes.
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2:35pm–3:15pm Thursday, September 12, 2019
Location: LL21 E/F
Secondary topics:
Design, Interfaces, and UX
Michael Radwin (Intuit)
Average rating:
(5.00, 4 ratings)
Design thinking is a methodology for creative problem-solving developed at the Stanford d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. Michael Radwin prepares a recipe for how to apply design thinking to the development of AI/ML products.
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2:35pm–3:15pm Thursday, September 12, 2019
Location: Expo Hall 3
Secondary topics:
Deep Learning tools
Brennan Saeta (Google)
Average rating:
(5.00, 2 ratings)
Swift for TensorFlow is a next-generation machine learning and differential programming framework that unlocks new domains and applications. Brennan Saeta leads you through the motivations for Swift, the benefits of this toolchain, and how to use Swift for TensorFlow in your projects.
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2:35pm–3:15pm Thursday, September 12, 2019
Location: 230 B
Secondary topics:
Machine Learning
Manasi Vartak (Verta.ai)
Average rating:
(3.50, 2 ratings)
Enterprises are investing heavily in integrating AI/ML into their business, and yet it remains challenging to transform these research-oriented initiatives into revenue-driving functions due to a lack of efficient tooling. Manasi Vartak examines key methods that enterprise AI teams can leverage with regard to driving revenue, including A/B testing, data pipelines, and reproducibility.
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4:00pm–4:40pm Thursday, September 12, 2019
Location: 230 C
Secondary topics:
Deep Learning,
Ethics, Security, and Privacy,
Machine Learning,
Reinforcement Learning,
Text, Language, and Speech
Jisheng Wang (Mist Systems)
Increased complexity and business demands continue to make enterprise network operation more challenging. Jisheng Wang outlines the architecture of the first autonomous network operation solution along with two examples of ML-driven automated actions. He also details some of his experiences and the lessons he learned applying ML, DL, and AI to the development of SaaS-based enterprise solutions.
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4:00pm–4:40pm Thursday, September 12, 2019
Location: 230 B
Average rating:
(4.00, 1 rating)
Bing in Microsoft runs large, complex workflows and services, but there was no existing solutions that met its needs. So it created and open-sourced FrameworkLauncher. Kai Liu, Yuqi Wang, and Bin Wang explore the solution, built to orchestrate workloads on YARN through the same interface without changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs.
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4:50pm–5:30pm Thursday, September 12, 2019
Location: 230 A
Secondary topics:
Deep Learning,
Machine Learning,
Temporal data and time-series
Alex (Tianchu) Liang (American Tire Distributors)
Average rating:
(4.00, 1 rating)
Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling.
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4:50pm–5:30pm Thursday, September 12, 2019
Location: 230 B
Secondary topics:
Machine Learning
Jonathan Peck (GitHub)
Average rating:
(5.00, 1 rating)
ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.
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