Course details

Machine Learning with TensorFlow and Cloud ML

Machine Learning with TensorFlow and Cloud ML


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Cloud ML combines the Google Cloud Platform with TensorFlow to create models at scale. In this course, you'll learn about concepts behind TensorFlow and scaling, as well as training models locally and in the cloud.

Target Audience
Data professionals who are responsible for provisioning and optimizing big data solutions, and data enthusiasts getting started with Google Cloud Platform

Prerequisites
None

Expected Duration (hours)
1.5

Lesson Objectives

Machine Learning with TensorFlow and Cloud ML

  • start the course
  • describe concepts of machine learning in relation to GCP
  • describe the use of datasets in GCP
  • demonstrate how to load a dataset for Cloud ML in GCP
  • describe the use of TensorFlow with machine learning
  • run a TensorFlow Python program in Google Cloud Shell
  • use TensorFlow to run a local trainer
  • demonstrate how to use TensorBoard to inspect TensorFlow logs and graphs
  • run a local trainer in distributed mode
  • demonstrate how to run a single-instance trainer in the cloud
  • inspect Stackdriver logs for an ML Engine job run in the cloud
  • describe the process of scaling with Cloud ML
  • demonstrate how to run distributed training in the cloud
  • use hyperparameter tuning to help maximize a model's predictive accuracy
  • describe the TensorFlow operations that are used for big data
  • Course Number:
    cl_gcde_a12_it_enus

    Expertise Level
    Intermediate