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: Blenheim Room - Palace Suite
Secondary topics:  Machine Learning
Ira Cohen (Anodot)
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: Buckingham Room - Palace Suite
Danielle Dean (Microsoft), 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: Windsor Suite
Robert Nishihara (UC Berkeley), Philipp Moritz (University of California, Berkeley), Ion Stoica (UC Berkeley), Eric Liang (University of California, Berkeley, RISELab)
Building AI applications is challenging, and building the next generation is even more challenging. Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms. Read more.
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13:3017:00
Location: Blenheim Room - Palace Suite
Robert Crowe (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 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. Read more.
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13:3017:00
Location: Buckingham Room - Palace Suite
Vijay Srinivas Agneeswaran (Walmart Labs), Pramod Singh (Publicis Sapient), Akshay Kulkarni (Publicis Sapient)
An estimated 80% of data generated is an unstructured format, such as text, 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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