14–17 Oct 2019

Schedule: Machine Learning tools sessions

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9:0012:30 Tuesday, 15 October 2019
Location: Windsor Suite
Danielle Dean (iRobot), Mathew Salvaris (Microsoft), Wee Hyong Tok (Microsoft)
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.
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9:0012:30 Tuesday, 15 October 2019
Location: Blenheim Room - Palace Suite
Edward Oakes (UC Berkeley Electrical Engineering & Computer Sciences), Peter Schafhalter (UC Berkeley RISELab), Kristian Hartikainen (University of Oxford)
Average rating: *****
(5.00, 5 ratings)
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. Read more.
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13:3017:00 Tuesday, 15 October 2019
Location: Blenheim Room - Palace Suite
Robert Crowe (Google), Pedram Pejman (Google)
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. Read more.
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13:3017:00 Tuesday, 15 October 2019
Location: Windsor Suite
Sergey Ermolin (Amazon Web Services), Vineet Khare (Amazon Web Services)
Average rating: *....
(1.50, 4 ratings)
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. Read more.
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10:0010:15 Wednesday, 16 October 2019
Location: King's Suite
Kim Hazelwood (Facebook), Mohamed Fawzy (Facebook)
Average rating: ****.
(4.00, 12 ratings)
AI plays a key role in achieving Facebook's mission of connecting people and building communities. Nearly every visible product is powered by machine learning algorithms at its core, from delivering relevant content to making the platform safe. Kim Hazelwood and Mohamed Fawzy explain how applied ML has continued to change the landscape of the platforms and infrastructure at Facebook. Read more.
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11:0511:45 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Yan Zhang (Microsoft), Mathew Salvaris (Microsoft)
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. Read more.
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11:0511:45 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
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. Read more.
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11:5512:35 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Chang Liu (Georgian Partners ), Ji Chao Zhang (Georgian Partners)
Average rating: ****.
(4.33, 3 ratings)
The world is increasingly data driven, and people have developed an awareness and concern for their data. Chang Liu and Ji Chao Zhang examine differential privacy—the component of the TensorFlow Privacy library that allows users to train differentially private logistic regression and support vector machines—along with real-world use cases and demonstrations for how to apply the tools. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Paris Buttfield-Addison (Secret Lab), Tim Nugent (Lonely Coffee)
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. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
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. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: Windsor Suite
Average rating: *****
(5.00, 3 ratings)
In the rapidly changing world of AI, adopting the right design principles is key. From data scientists and business users to client end users, IBM Watson always seeks to augment their capabilities. Ariadna Font Llitjós examines how IBM Watson applies ethical AI and user-centered design principles from the beginning and leverages them throughout the product development cycle. Read more.
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14:3515:15 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Danielle Dean (iRobot), Wee Hyong Tok (Microsoft), Mathew Salvaris (Microsoft)
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. Read more.
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14:3515:15 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
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. Read more.
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14:3515:15 Wednesday, 16 October 2019
Location: Westminster Suite
Tobias Martens (whoelse.ai)
Average rating: ***..
(3.50, 2 ratings)
More than 50% of all interactions between humans and machines are expected to be speech-based by 2022. The challenge: Every AI interprets human language slightly different. Tobias Martens details current issues in NLP interoperability and uses Chomsky's theory of universal hard-wired grammar to outline a framework to make the human voice in AI universal, accountable, and computable. Read more.
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16:0016:40 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
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. Read more.
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16:5017:30 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
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. Read more.
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16:5017:30 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
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. Read more.
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9:059:20 Thursday, 17 October 2019
Location: King's Suite
Zhe Zhang (LinkedIn)
Average rating: ****.
(4.12, 8 ratings)
From people you may know (PYMK) to economic graph research, machine learning is the oxygen that powers how LinkedIn serves its 630M+ members. Zhe Zhang provides you with an architectural overview of LinkedIn’s typical machine learning pipelines complemented with key types of ML use cases. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Westminster Suite
Siddha Ganju (NVIDIA), Meher Kasam (Square)
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. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Julien Simon (AWS)
Average rating: ****.
(4.86, 7 ratings)
Many natural language processing (NLP) tasks require each word in the input text to be mapped to a vector of real numbers. Julien Simon explores word vectors, why they’re so important, and which are the most popular algorithms to compute them (Word2Vec, GloVe, BERT). You'll get to see how to solve typical NLP problems through several demos by either computing embeddings or reusing pretrained ones. Read more.
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13:4514:25 Thursday, 17 October 2019
Location: Westminster Suite
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). Read more.
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13:4514:25 Thursday, 17 October 2019
Location: King's Suite - Sandringham
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. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: Westminster Suite
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. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: King's Suite - Sandringham
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. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: Windsor Suite
Paco Nathan (derwen.ai)
Average rating: ****.
(4.50, 2 ratings)
Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Paris Buttfield-Addison (Secret Lab), Tim Nugent (Lonely Coffee)
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. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Tuhin Sharma (Binaize), Bargava Subramanian (Binaize)
Average rating: ****.
(4.50, 2 ratings)
There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learning—which is privacy preserving and doesn't require data to be moved to the cloud—for data quality and cybersecurity. Read more.
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16:5017:30 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Jim Dowling (Logical Clocks), Ajit Mathews (AMD)
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. Read more.
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16:5017:30 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
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. Read more.

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