14–17 Oct 2019
Buckingham Room - Palace Suite
9:00
Herding cats: Product management in the machine learning era
Ira Cohen (Anodot), Arun Kejariwal (Independent)
13:30
Text analytics 101: Deep learning and attention networks all the way to production
pramod singh (Walmart Labs ), Akshay Kulkarni (Publicis Sapient)
Blenheim Room - Palace Suite
9:00
Scalable AI and reinforcement learning with Ray
Edward Oakes (UC Berkeley Electrical Engineering & Computer Sciences), Peter Schafhalter (UC Berkeley RISELab), Kristian Hartikainen (University of Oxford)
Windsor Suite
9:00
Training and deploying Python models on Azure
Danielle Dean (iRobot), Mathew Salvaris (Microsoft), Wee Hyong Tok (Microsoft)
13:30
Using reinforcement learning to build recommendation systems with AWS SageMaker RL
Sergey Ermolin (Amazon Web Services), Vineet Khare (Amazon Web Services)
Westminster Suite
Hilton Meeting Room 1/2
Hilton Meeting Room 3/4
Park Suite
12:30
Lunch sponsored by Intel
| Room: Fiamma Restaurant
8:00
Morning Coffee
| Room: Hilton Meeting Room Foyer, Mezzanine, Palace & Windsor Suite Foyer
10:30
Morning Break
| Room: Hilton Meeting Room Foyer, Mezzanine, Palace & Windsor Suite Foyer
15:00
Afternoon Break
| Room: Hilton Meeting Room Foyer, Mezzanine, Palace & Windsor Suite Foyer
9:00-12:30 (3h 30m)
AI Business Summit, Executive Briefing/Best Practices
Machine Learning
Herding cats: Product management in the machine learning era
Ira Cohen (Anodot), Arun Kejariwal (Independent)
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 the cycle of developing ML-based capabilities (or entire products) and the role of the (product) manager in each step of the cycle.
13:30-17:00 (3h 30m)
Implementing AI
Deep Learning, Machine Learning, Text, Language, and Speech
Text analytics 101: Deep learning and attention networks all the way to production
pramod singh (Walmart Labs ), Akshay Kulkarni (Publicis Sapient)
An estimated 80% of data generated is an unstructured format, such as text, an image, audio, or video. Vijay Srinivas Agneeswaran, Pramod Singh, and Akshay Kulkarni explore how to create a language model that generates natural language text by implementing and forming a recurrent neural network and attention networks built on top of TensorFlow 2.0.
9:00-12:30 (3h 30m)
Machine Learning, Machine Learning tools, Reinforcement Learning
Scalable AI and reinforcement learning with Ray
Edward Oakes (UC Berkeley Electrical Engineering & Computer Sciences), Peter Schafhalter (UC Berkeley RISELab), Kristian Hartikainen (University of Oxford)
Edward Oakes, Peter Schafhalter, and Kristian Hartikainen take a deep dive into Ray, a new distributed execution framework for distributed AI applications developed by machine learning and systems researchers at RISELab, and explore Ray’s API and system architecture and sharing application examples, including several state-of-the-art distributed training, hyperparameter search, and RL algorithms.
13:30-17:00 (3h 30m)
Implementing AI
Deep Learning tools, Machine Learning tools
TFX: Production ML pipelines with TensorFlow
Robert Crowe (Google), Pedram Pejman (Google)
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 and Pedram Pejman explore Google's 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.
9:00-12:30 (3h 30m)
Implementing AI
Computer Vision, Machine Learning, Machine Learning tools
Training and deploying Python models on Azure
Danielle Dean (iRobot), Mathew Salvaris (Microsoft), Wee Hyong Tok (Microsoft)
Danielle Dean, Mathew Salvaris, and Wee Hyong Tok outline the recommended ways to train and deploy Python models on Azure, ranging from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.
13:30-17:00 (3h 30m)
Machine Learning, Machine Learning tools, Reinforcement Learning
Using reinforcement learning to build recommendation systems with AWS SageMaker RL
Sergey Ermolin (Amazon Web Services), Vineet Khare (Amazon Web Services)
Sergey Ermolin and Vineet Khare provide a step-by-step overview on how to implement, train, and deploy a reinforcement learning (RL)-based recommender system with real-time multivariate optimization. They show you how leverage RL to implement a recommender system that optimizes an advertisement message that promotes adoption of merchant's services.
9:00-17:00 (8h)
Deep Learning, Deep Learning tools, Machine Learning, Machine Learning tools
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. Join Rich Ott to get the knowledge you need to build deep learning models using real-world datasets and PyTorch.
9:00-17:00 (8h)
Machine Learning
AI for executives (Day 2)
Angie Ma and Richard Sargeant offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.
9:00-17:00 (8h)
Convolutional neural networks for image recognition in Keras and TensorFlow (Day 2)
Convolutional neural networks (CNNs) are the basis of many algorithms that deal with images, from image recognition and classification to object detection. Using practical examples, Umberto Michelucci walks you through developing convolutional neural networks, using pretrained networks, and even teaching a network to paint. TensorFlow or Keras will be used for all examples.
9:00-17:00 (8h)
Deep Learning, Deep Learning tools, Machine Learning, Machine Learning tools
Deep learning with TensorFlow (Day 2)
The TensorFlow library provides computational graphs with automatic parallelization across resources—ideal architecture for implementing neural networks. Michael Cullan 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.
17:00-18:30 (1h 30m)
Ignite AI London
If you had five minutes on stage, what would you say? What if you only got 20 slides, and they rotated automatically after 15 seconds? Would you pitch a project? Launch a website? Teach a hack? We’ll find out at our Ignite event at AI London.
12:30-13:30 (1h)
Break: Lunch sponsored by Intel
8:00-9:00 (1h)
Break: Morning Coffee
10:30-11:00 (30m)
Break: Morning Break
15:00-15:30 (30m)
Break: Afternoon Break
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Elite Sponsors
Strategic Sponsor
Exabyte Sponsor
Impact Sponsor
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