14–17 Oct 2019
Schedule: Implementing AI sessions
9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Westminster Suite
Secondary topics:
Deep Learning,
Deep Learning tools,
Machine Learning
Rich Ott (The Pragmatic Institute)
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.
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9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Park Suite
Secondary topics:
Deep Learning,
Deep Learning tools,
Machine Learning
Michael Cullan (Pragmatic Institute)
Average rating:
(4.00, 1 rating)
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.
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9:00–12:30 Tuesday, 15 October 2019
Location: Windsor Suite
Secondary topics:
Computer Vision,
Machine Learning,
Machine Learning tools
Average rating:
(4.33, 6 ratings)
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.
Read more.
13:30–17:00 Tuesday, 15 October 2019
Location: Buckingham Room - Palace Suite
Secondary topics:
Deep Learning,
Machine Learning,
Text, Language, and Speech
Average rating:
(2.40, 10 ratings)
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.
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13:30–17:00 Tuesday, 15 October 2019
Location: Blenheim Room - Palace Suite
Secondary topics:
Deep Learning tools,
Machine Learning tools
Average rating:
(2.90, 10 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 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.
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11:05–11:45 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Average rating:
(4.00, 3 ratings)
Steve Flinter and Ahmed Menshaw explore the work that Mastercard Labs undertook to build an end-to-end machine learning pipeline, suitable for both R&D and production, using Kubernetes and Kubeflow. They demonstrate how the pipeline can be defined, configured, connected to a data streaming service, and used to train and deploy a model, which can be exposed for inference via an API.
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11:05–11:45 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Hardware,
Machine Learning tools,
Mobile Computing, IoT, Edge
Average rating:
(3.67, 3 ratings)
When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices.
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11:05–11:45 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Secondary topics:
Deep Learning,
Deep Learning tools,
Ethics, Security, and Privacy,
Machine Learning,
Machine Learning tools,
Mobile Computing, IoT, Edge
Alex Ingerman (Google)
Average rating:
(4.29, 7 ratings)
Federated learning is the approach of training ML models across many devices without collecting the data in a central location. Alex Ingerman explores learning concepts and the use cases for decentralized machine learning, drawing on Google's real-world deployments. You'll learn how to build your first federated models with the open source TensorFlow Federated.
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13:45–14:25 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Secondary topics:
Computer Vision,
Hardware,
Machine Learning,
Machine Learning tools,
Mobile Computing, IoT, Edge
Average rating:
(4.75, 8 ratings)
On-device ML and AI is the future for privacy-conscious, cloud-averse users of modern smartphones. Paris Buttfield-Addison and Tim Nugent explore what's possible using CoreML, Swift, and associated frameworks in tandem with the powerful ML-tuned silicon in modern Apple iOS hardware. They demonstrate and create ML and AI features with Swift to show how much you can do without touching the cloud.
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13:45–14:25 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Deep Learning tools,
Machine Learning tools
Antje Barth (AWS)
Average rating:
(4.71, 14 ratings)
Container and cloud native technologies around Kubernetes have become the de facto standard in modern ML and AI application development. Antje Barth examines common architecture blueprints and popular technologies used to integrate AI into existing infrastructures and explains how you can build a production-ready containerized platform for deep learning.
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13:45–14:25 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Secondary topics:
Data, Data Networks, Data Quality,
Deep Learning,
Machine Learning,
Text, Language, and Speech
Douglas Calegari (Independent)
Average rating:
(4.00, 3 ratings)
Douglas Calegari details a solution that classifies and routes emails coming into a busy insurance service center. Join in to discover how his team evaluated NLP models, leveraged various techniques to increase classification and entity recognition accuracy, designed a scalable end-to-end machine learning data pipeline, and integrated them into an existing transactional system.
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13:45–14:25 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Secondary topics:
Machine Learning,
Reinforcement Learning
Average rating:
(4.67, 3 ratings)
Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.
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13:45–14:25 Wednesday, 16 October 2019
Location: Westminster Suite
Secondary topics:
Ethics, Security, and Privacy,
Machine Learning
Martin Goodson (Evolution AI)
Average rating:
(4.75, 4 ratings)
Data leakage occurs when the model gains access to data that it shouldn't have. AI systems can fail catastrophically in production if leakage is not dealt with properly. Martin Goodson details the four main manifestations of data leakage and explains how to recognize the warning signs. By mastering several key scientific principles, you can mitigate the risk of failure.
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14:35–15:15 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Secondary topics:
Machine Learning tools
Ahmed Kamal (Careem)
Average rating:
(4.00, 7 ratings)
Every day Careem’s platform relies on machine learning (ML) in production to enable the movement of millions of its users. Ahmed Kamal outlines the challenges Careem faced while productionizing ML on scale and explains how to build an in-house ML platform that facilitates development and fast deployment of scalable ML services and accelerates the impact of ML everywhere.
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14:35–15:15 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Machine Learning,
Machine Learning tools
Average rating:
(4.00, 2 ratings)
Dive into the the newly released GitHub repository for recommended ways to train and deploy models on Azure with Danielle Dean, Wee Hyong Tok, and Mathew Salvaris. The repository ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.
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14:35–15:15 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Secondary topics:
Deep Learning,
Health and Medicine,
Machine Learning,
Text, Language, and Speech
Abhishek Kumar (Publicis Sapient)
Abhishek Kumar outlines how to industrialize capsule networks by detailing capsule networks and how capsule networks help handle spatial relationships between objects in an image and how to apply them to text analytics and tasks such as NLU or summarization. Join in to see a scalable, productionizable implementation of capsule networks over KubeFlow.
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16:00–16:40 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Deep Learning tools,
Hardware,
Machine Learning tools
Thomas Phelan (HPE BlueData)
Average rating:
(4.50, 2 ratings)
Today, organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning, but these new technologies come with their own challenges. Thomas Phelan demonstrates the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment.
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16:50–17:30 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Secondary topics:
Computer Vision,
Deep Learning,
Hardware,
Machine Learning,
Mobile Computing, IoT, Edge
Adam Grzywaczewski (NVIDIA)
Average rating:
(4.25, 12 ratings)
Developing perception algorithms for autonomous vehicles is incredibly difficult, as they need to operate in thousands of driving conditions and locations. Adam Grzywaczewski explores the challenges involved in data collection, processing, and management, as well as model development and validation. He also provides an overview of the necessary hardware and software infrastructure.
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16:50–17:30 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Machine Learning,
Machine Learning tools
Bruno Wassermann (IBM Research)
Average rating:
(4.00, 2 ratings)
Imagine there's a new version of your complex machine learning pipeline, but you need to make sure it doesn't negatively impact the performance of large numbers of existing customer models in production. Bruno Wassermann explains how IBM Research tackled the challenge for the natural language understanding layer of the IBM Watson Assistant service and demonstrates a new tool called Clue.
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16:50–17:30 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Secondary topics:
Health and Medicine,
Machine Learning,
Machine Learning tools
James Fletcher (Grakn)
Average rating:
(3.50, 2 ratings)
Statistical approaches alone are not sufficient to tackle the complexity of AI challenges today. Being smarter with the data we already have is critical to achieving machine understanding of any complex domain. James Fletcher explains how knowledge graph convolutional networks (KGCNs) demonstrate the usefulness of combining a connectionist deep learning approach with a symbolic approach.
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11:05–11:45 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Secondary topics:
Ethics, Security, and Privacy,
Machine Learning,
Mobile Computing, IoT, Edge,
Temporal data and time-series
Zaid Tashman (Accenture Labs)
Average rating:
(5.00, 3 ratings)
Today traditional approaches to predictive maintenance fall short. Zaid Tashman dives into a novel approach to predict rare events using a probabilistic model, the mixed membership hidden Markov model, highlighting the model's interpretability, its ability to incorporate expert knowledge, and how the model was used to predict the failure of water pumps in developing countries.
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11:55–12:35 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Secondary topics:
Hardware,
Machine Learning,
Mobile Computing, IoT, Edge
Alasdair Allan (Babilim Light Industries)
Average rating:
(4.50, 4 ratings)
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. Alasdair Allan dives deep into how using deep learning can be very energy efficient and allows you to make sense of sensor data in real time.
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11:55–12:35 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Secondary topics:
Machine Learning
Average rating:
(4.00, 2 ratings)
GoFood, Gojek's food delivery product, is one of the largest of its kind in the world. Jewel James and Mudit Maheshwari explain how they prototyped the search framework that personalizes the restaurant search results by using ML to learn what constitutes a relevant restaurant given a user's purchasing history.
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11:55–12:35 Thursday, 17 October 2019
Location: Westminster Suite
Secondary topics:
Computer Vision,
Deep Learning,
Deep Learning tools,
Hardware,
Machine Learning tools,
Mobile Computing, IoT, Edge
Average rating:
(4.80, 5 ratings)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices.
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13:45–14:25 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Machine Learning tools,
Text, Language, and Speech
Tyler Dunn (Rasa)
Average rating:
(4.00, 4 ratings)
AI assistants are getting a great deal of attention from the industry and research. However, the majority of assistants built to this day are still developed using a state machine and a set of rules. That doesn’t scale in production. Tyler Dunn explores how to build AI assistants that go beyond FAQ interactions using machine learning and open source tools.
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13:45–14:25 Thursday, 17 October 2019
Location: Westminster Suite
Secondary topics:
Machine Learning tools,
Text, Language, and Speech
Holger Kyas (Open Group, Helvetia Insurances, University of Applied Sciences)
Average rating:
(1.00, 1 rating)
Holger Kyas details the AI multicloud broker, which is triggered by Amazon Alexa and mediates between AWS Comprehend (Amazon), Azure Text Analytics (Microsoft), GCP Natural Language (Google), and Watson Tone Analyzer (IBM) to compare and analyze sentiment. The extended AI part generates new sentences (e.g., marketing slogans) with a recurrent neural network (RNN).
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14:35–15:15 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Computer Vision,
Deep Learning tools,
Machine Learning tools
Laurence Moroney (Google)
Average rating:
(4.86, 7 ratings)
Laurence Moroney explores how to go from wondering what machine learning (ML) is to building a convolutional neural network to recognize and categorize images. With this, you'll gain the foundation to understand how to use ML and AI in apps all the way from the enterprise cloud down to tiny microcontrollers using the same code.
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14:35–15:15 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Secondary topics:
Deep Learning,
Machine Learning
Carlos Rodrigues (Siemens)
Average rating:
(5.00, 3 ratings)
An evolving landscape of cyber threats demands innovation. It's time to bring AI to the fight. Carlos Rodrigues explains why it's mandatory to use bleeding-edge AI in production to improve threat detection in a worldwide company such as Siemens. The corporate network has more than 500,000 endpoint and more than 370,000 employees. The attack vectors are endless; thus, legacy approaches don't scale.
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14:35–15:15 Thursday, 17 October 2019
Location: Westminster Suite
Secondary topics:
Machine Learning tools,
Mobile Computing, IoT, Edge
Jameson Toole (Fritz AI)
Average rating:
(5.00, 1 rating)
Getting machine learning models ready for use on device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce aren’t small enough or fast enough to ship. Jameson Toole walks you through optimization, pruning, and compression techniques to keep app sizes small and inference speeds high.
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16:00–16:40 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Machine Learning,
Machine Learning tools,
Reinforcement Learning
Average rating:
(4.80, 5 ratings)
You're building a high-volume, expensive, robot-driven warehouse. Your robots need to get to the right place quickly, find the right item, and sort it to the right place without colliding with each other, the shelves, or people. But you don't have any robots, and you need to start writing the logic and training them. Paris Buttfield-Addison and Tim Nugent outline how to use a simulation to do it.
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16:50–17:30 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Secondary topics:
Deep Learning tools,
Hardware,
Machine Learning tools
Average rating:
(5.00, 1 rating)
The Radeon open ecosystem (ROCm) is an open source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. Jim Dowling and Ajit Mathews outline how the open source Hopsworks framework enables the construction of horizontally scalable end-to-end machine learning pipelines on ROCm-enabled GPUs.
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16:50–17:30 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Secondary topics:
Machine Learning,
Machine Learning tools,
Temporal data and time-series
Vignesh Gopakumar (United Kingdom Atomic Energy Authority)
Vignesh Gopakumar explores image mapping of the temporal evolution of physics parameters as plasma interacts with the reactor wall using a data-inferred approach. The model captures how parameters such as temperature and density evolve across space and time. By analyzing the patterns found in simulation data, the model learns the existing physics relations implicitly defined within the data.
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