In this workshop, we walk through the process of building machine learning models with TensorFlow. We cover data exploration, feature engineering, model creation, training, evaluation and deployment.
Explore the AWS machine learning platform using Amazon SageMaker
Wenming Ye (Amazon Web Services), Miro Enev (NVIDIA)
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.
Enterprise and organizational adoption, Extensions and customization, Usage and application
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.
Reproducible Research Best Practices (highlighting Kaggle Kernels)
Rachael Tatman (Kaggle)
In this workshop, we’ll take an existing research project and make it fully reproducible using Kaggle Kernels. This workshop will include hands-on instruction and best practices for each of the three components necessary for completely reproducible research.