October 28–31, 2019
2-Day Training Courses
All training courses take place 9:00am–5:00pm, Monday, October 28–Tuesday, October 29. In order to maintain a high level of hands-on learning and instructor interaction, each training course is limited in size.
Participants should plan to attend both days of this 2-day training course. To attend training courses, you must register for a Platinum or Training pass; does not include access to tutorials on Tuesday.
Monday, October 28 - Tuesday, October 29
Location: Room 211
Shashank Prasanna (Amazon Web Services),
vikrant kahlir (Amazon Web Services),
Rama Thamman (Amazon Web Services),
Shreyas Subramanian (Amazon)
Average rating:
(3.00, 2 ratings)
Amazon Web Services (AWS) offers a breadth and depth of services to easily build, train, and deploy TensorFlow models. Shashank Prasanna, Vikrant Kahlir, and Rama Thamman give you hands-on experience working with these services.
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Location: Room 204
Robert Schroll (The Data Incubator)
The TensorFlow library provides for the use of computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll introduces TensorFlow's capabilities in Python, moving 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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Location: Room 212
Maxim Lukiyanov (Microsoft),
Vaidyaraman Sambasivam (Microsoft),
Mehrnoosh Sameki (MERS) (Microsoft),
Santhosh Pillai (Microsoft)
Maxim Lukiyanov, Vaidyaraman Sambasivam, Mehrnoosh Samekihow, and Santhosh Pillai demonstrate how AzureML helps data scientists be more productive when working through developing TensorFlow models for production. You'll see the whole model development lifecycle from training to deployment and ML ops to model interpretability.
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Location: Room 203
Valliappa Lakshmanan (Google)
Valliappa Lakshmanan shows you how to use Google Cloud Platform to design and build machine learning (ML) models and how to deploy them into production. You'll walk through the process of building a complete machine learning pipeline from ingest and exploration to training, evaluation, deployment, and prediction.
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Location: Great America Meeting Room 3
Aurélien Geron (Kiwisoft)
Average rating:
(5.00, 2 ratings)
Aurélien Géron dives into creating production ML pipelines with TensorFlow Extended (TFX) and using TFX to move from ML coding to ML engineering. You'll walk through the basics and put your first pipeline together, then learn how to customize TFX components and perform deep analysis of model performance.
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