Machine Learning with TensorFlow and Cloud ML

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

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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