Workshop: Serverless Machine Learning with TensorFlow

Learn from Amy Unruh from Google Cloud Platform team who has an amazing expertise in AI & ML
10th March 2018 (10:00 AM to 06:00 PM), Park Plaza, Bangalore

Overview

This hands-on workshop walks through the process of building machine learning models with TensorFlow. It covers data exploration, feature engineering, model creation, training, evaluation and deployment.

Topics Covered / What You Will Learn

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. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and deploying ML models

  • Getting started with machine learning: We will discuss how to explore and split large data sets correctly - for this tutorial we will be using SQL and Pandas on BigQuery and Cloud Datalab.
  • Building ML models with TensorFlow: The wide-and-deep machine learning model in TensorFlow will be developed on a small sample locally.
  • Feature engineering: We will look at ways to bring human insight into ML problems through better feature representations and feature engineering.
  • Distributed model training, inference and deployment: We’ll scale out the model training to multiple machine. The trained model will be deployed as a REST microservice and predictions invoked from a web application

[This workshop will be conducted on Google Cloud Platform (GCP) and will use GCP's infrastructure to run TensorFlow. All you need is a laptop with a modern browser.]

Amy

Amy Unruh is a developer relations engineer for the Google Cloud Platform, where she focuses on machine learning and data analytics as well as other Cloud Platform technologies. Amy has an academic background in CS/AI and has also worked at several startups, done industrial R&D, and published a book on App Engine.