14–17 Oct 2019

Tutorials

These expert-led presentations on Tuesday, 15 October give you a chance to dive deep into the subject matter. Please note: to attend tutorials, you must be registered for a Gold or Silver pass; does not include access to training courses on Monday or Tuesday.

Tuesday, 15 October

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9:0012:30
Location: Buckingham Room - Palace Suite
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. Read more.
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9:0012:30
Location: Windsor Suite
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. Read more.
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9:0012:30
Location: Blenheim Room - Palace Suite
Edward Oakes (UC Berkeley Electrical Engineering & Computer Sciences), Peter Schafhalter (UC Berkeley RISELab), Kristian Hartikainen (UC Berkeley Robotics and AI Lab)
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
Location: Blenheim Room - Palace Suite
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. Read more.
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13:3017:00
Location: Buckingham Room - Palace Suite
Pramod Singh (Publicis Sapient), 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. Read more.
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13:3017:00
Location: Windsor Suite
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. Read more.

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