Brought to you by NumFOCUS Foundation and O’Reilly Media
The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY
 
Concourse A
Add Serverless machine learning with TensorFlow to your personal schedule
9:00am Serverless machine learning with TensorFlow Carl Osipov (Google)
Concourse B
Add Explore the AWS machine learning platform using Amazon SageMaker to your personal schedule
9:00am Explore the AWS machine learning platform using Amazon SageMaker Wenming Ye (Amazon Web Services), Miro Enev
Concourse E
Add  Hands-on data science with Python to your personal schedule
9:00am Hands-on data science with Python Zachary Glassman (The Data Incubator)
Concourse F
12:30pm Lunch | Room: Murray Hill A
9:00am-5:00pm (8h) Training
Serverless machine learning with TensorFlow
Carl Osipov (Google)
Carl Osipov walks you through the process of building machine learning models with TensorFlow. You'll learn about data exploration, feature engineering, model creation, training, evaluation, deployment, and more.
9:00am-5:00pm (8h) Training
Explore the AWS machine learning platform using Amazon SageMaker
Wenming Ye (Amazon Web Services), Miro Enev
Machine learning and IoT projects are increasingly common at enterprises and startups alike and have been the key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev lead a hands-on deep dive into the AWS machine learning platform, using Project Jupyter-based Amazon SageMaker to build, train, and deploy ML/DL models to the cloud and AWS DeepLens.
9:00am-5:00pm (8h) Enterprise and organizational adoption, Extensions and customization, Usage and application Training
Hands-on data science with Python
Zachary Glassman (The Data Incubator)
Zachary Glassman leads a hands-on dive into building intelligent business applications using machine learning, walking you through all the steps of developing a machine learning pipeline. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend these models into two applications from real-world datasets.
9:00am-5:00pm (8h) Training
Reproducible research best practices (highlighting Kaggle Kernels)
Rachael Tatman (Kaggle)
Rachael Tatman shows you how to take an existing research project and make it fully reproducible using Kaggle Kernels. You'll learn best practices for and get hands-on experience with each of the three components necessary for completely reproducible research.
12:30pm-1:30pm (1h)
Break: Lunch